CN106504237A

CN106504237A - Determine method and the image acquiring method of matching double points

Info

Publication number: CN106504237A
Application number: CN201610916842.0A
Authority: CN
Inventors: 胡扬; 牛杰; 徐亮; 王汉禹
Original assignee: Shanghai United Imaging Healthcare Co Ltd
Current assignee: Shanghai United Imaging Healthcare Co Ltd
Priority date: 2016-09-30
Filing date: 2016-09-30
Publication date: 2017-03-15
Also published as: CN106504229B; CN106504229A; CN106548492A

Abstract

The method for determining matching double points.Methods described includes：Pyramid decomposition, the first difference pyramid of construction and the second difference pyramid is carried out to the first image and the second image；The each layer of the first difference pyramid is traveled through, the extreme point that searches in every layer of difference pyramid, each layer of the second difference pyramid of traversal, the extreme point that searches in every layer of difference pyramid；Remove in the pyramidal extreme point of the first difference the characteristic point in the first image is obtained for the pixel in the first image strong edge；Remove in the pyramidal extreme point of the second difference the characteristic point in the second image is obtained for the pixel in the second image strong edge；The characteristic vector of characteristic point in characteristic vector and the second image based on characteristic point in the first image, carries out bi-directional matching and generates initial matching point pair to characteristic point in the first image and the second image；The matching double points for removing initial matching point centering mistake obtain matching double points.This programme complexity is low, the simple and quick matching double points for determining that accuracy is high of amount of calculation I.

Description

Determine method and the image acquiring method of matching double points

Technical field

The present invention relates to technical field of image processing, the detection method of characteristic point and device in more particularly to a kind of image, Determine method and device, image acquiring method and the device and X-ray shooting system of matching double points.

Background technology

Digital X-ray photography (DR, Digital Radiography) equipment is computer digital image treatment technology and X A kind of advanced armarium that ray irradiation technology combines and formed.Digital X-ray photographic equipment because its radiation dose is little, The quality of image is high, the accuracy of the recall rate of disease and diagnosis is higher and be widely used.

In orthopaedic diseases such as diagnosis vertebra prolapse, lateral bending, lower limb malformations, or carry out the reduction of the fracture, Joint shift, osteotomy When art and pedicle screw are inserted, the auxiliary of medical imaging device is needed, and X-ray equipment is due to its low cost, dosage Little and the advantages of noinvasive is visualized can be realized, become the preferred unit of above-mentioned diagnosis at present.But as X-ray sets Standby restriction in terms of panel size, its areas imaging are difficult to cover complete vertebra or limbic areas.Now, generally by right Image sequence is accurately spliced using image processing algorithm, is provided for doctor by vertebra or long bone of limbs collection image sequence Accurate image information, to complete the diagnosis to above-mentioned orthopaedic disease.

When gathering image by X-ray equipment, the adjacent human dissection position for shooting twice is different, therefore adopt Dosage is also different, and therefore between adjacent image, the gray difference of overlapping region is also larger.And during shooting image, for keeping away Exempt from patient by excessive radiation dose, it will usually unnecessary primary X ray is screened off to by beam-defining clipper, which can be by roentgenogram Launched field is limited in required minimum zone, but the region that beam-defining clipper is covered in image can be interfered to accurate splicing.In addition, In order to protect person under inspection, therefore the reduction radiation dose that can also try one's best in shooting process shoots noise in the radioscopic image of acquisition Disturb larger, poor in image definition.And person under inspection is also difficult to hold one's breath always in multiple image process are obtained, have unavoidably Slight breathing is slight mobile, above-mentioned refer to the precision of stitching algorithm will have been affected.Additionally, being to avoid requirement from receiving Inspection person's long-time is remained stationary as and is held one's breath, and reduces the waiting time of doctor, improves diagnosis efficiency, also requires that image mosaic is calculated Method has execution speed quickly.

Accordingly, it is capable to the image of quick acquisition splicing high precision is the technological difficulties in image mosaic field.Existing to X ray The splicing of image is generally using feature based, based on gray scale, the joining method based on transform domain.For the image of feature based is spelled For connecing method, which carries out pretreatment first to image subject to registration, extracts specific feature set；Then according to similarity measurement letter Number, mates to the description of feature set, and then realizes the location matches of image.The image split-joint method of feature based is to image Grey scale change there is stronger robustness, and when carrying out image mosaic using the image split-joint method of feature based, be extracted The marked feature of image to be spliced, have compressed the quantity of information of image to a great extent, thus using the joining method amount of calculation compared with Little, execute speed.For the image split-joint method based on gray scale, which mainly uses the half-tone information of image, choosing A cost function that can suitably measure similarity degree between image is selected, then certain search strategy is adopted, taking makes the phase Like the parameter value that property cost function value obtains the correspondent transform model being most worth.But the image split-joint method based on gray scale is searched for most Than larger, algorithm execution time is long for the process amount of calculation of excellent parameter, and the sensitivity to picture noise is strong, to gradation of image dependency Stronger, and the size of the gray difference between two image overlapping regions to be spliced will affect the degree of accuracy of splicing.And base It is then that two width images are transformed from a spatial domain to by frequency domain by fast Fourier transform in the image split-joint method of transform domain, leads to Cross the crosspower spectrum of two width images phase place directly calculate two width images translation, rotation and scaling corresponding parameter, with reality The splicing of existing image.But the image split-joint method based on transform domain is affected by image border significant difference and noise etc., having can Cannot can correctly splice parameter, therefore the image split-joint method more application for being typically based on transform domain is registering when splicing initial The determination of parameter.Therefore, the more at present joining method for adopting feature based is realizing the splicing to radioscopic image.But adopt Characteristic point when being spliced to radioscopic image with the image split-joint method of existing feature based, in stitching image is treated When being detected, the complexity of detection algorithm is high and detection speed is slow.In addition using the image mosaic of existing distinguished point based When method realizes image mosaic, the matching double points of mistake are still suffered from matching double points, and the accuracy of matching double points is low, and then causes When carrying out image mosaic with the image split-joint method of feature based, the splicing precision for splicing the image for obtaining is not high, and clinic is examined Break and have a certain impact.

Therefore, how with the characteristic point in the quick detection image of relatively low complexity, simple and accurately determine Matching double points, obtain the image of splicing high precision, become one of current problem demanding prompt solution.

Content of the invention

The problem to be solved in the present invention is to provide a kind of method for determining matching double points, with relatively low complexity and less The simple and quick accurate matching double points for determining that accuracy is high of amount of calculation, and a kind of image acquisition side of splicing high precision is provided Method.

For solving the above problems, technical solution of the present invention provides a kind of method for determining matching double points, including：

Pyramid decomposition is carried out to the first image and the second image, based on decomposition after the first image and the second image difference Construct the first difference pyramid diagram picture corresponding with described first image and the second difference corresponding with second image gold Word tower image；Described first image and second image are adjacent image；

Each layer of the first difference pyramid diagram picture is traveled through, the extreme point that searches in every layer of difference pyramid diagram picture, Each layer of the second difference pyramid diagram picture is traveled through, the extreme point that searches in every layer of difference pyramid diagram picture；The extreme value Point is associated with the absolute value of the gray value of the pixel in default neighborhood；

Remove in the extreme point of the first difference pyramid diagram picture for the pixel in described first image strong edge with Obtain the characteristic point in described first image；It is second image in the extreme point for removing the second difference pyramid diagram picture Pixel in strong edge is obtaining the characteristic point in second image；

The feature of the characteristic point in the characteristic vector of the characteristic point in based on described first image and second image to Amount, carries out bi-directional matching to generate initial matching point pair to the characteristic point in described first image and second image；

Remove the matching double points of the initial matching point centering mistake to obtain matching double points.

Optionally, the extreme point of the first difference pyramid diagram picture refers to every layer of difference pyramid diagram pixel as in The absolute value of gray value is the pixel of the maximum absolute value of the gray value of pixel in the default neighborhood of the pixel；Described The extreme point of two difference pyramid diagram pictures refers to that the absolute value of the gray value of pixel in every layer of difference pyramid diagram picture is described The pixel of the maximum absolute value of the gray value of pixel in the default neighborhood of pixel.

Optionally, the extreme point of the first difference pyramid diagram picture is referred in the first difference pyramid diagram picture and is revised Pixel P afterwards, revised pixel P are obtained after the position to pixel P is modified, and the pixel P is referred to In every tomographic image of the first difference pyramid diagram picture, the absolute value of the gray value of pixel is the default neighborhood of the pixel The pixel of the maximum absolute value of the gray value of middle pixel；The extreme point of the second difference pyramid diagram picture refers to described Revised pixel P in two difference pyramid diagram pictures, the revised pixel P is that the position of pixel P is repaiied Just obtaining afterwards, pixel P refers to the absolute of the gray value of pixel in every tomographic image of the second difference pyramid diagram picture It is worth the pixel for the maximum absolute value of the gray value of pixel in the default neighborhood of the pixel.

Optionally, the extreme point of the first difference pyramid diagram picture refer to the absolute value of the gray value to pixel P by According to the pixel P for being located at top N after order sequence from large to small, the pixel P refers to the first difference pyramid diagram In every tomographic image of picture, the absolute value of the gray value of pixel is the exhausted of the gray value of pixel in the default neighborhood of the pixel To the pixel that value is maximum；The extreme point of the second difference pyramid diagram picture refers to the absolute value of the gray value to pixel P According to the pixel P for being located at top N after order sequence from large to small, the pixel P refers to the second difference pyramid In every tomographic image of image, the absolute value of the gray value of pixel is the gray value of pixel in the default neighborhood of the pixel The pixel of maximum absolute value.

Optionally, described based on described first image in characteristic point characteristic vector and second image in feature The characteristic vector of point, carries out bi-directional matching to the characteristic point in described first image and second image to generate initial matching Point is to including：

Spy with the characteristic point in the characteristic vector of each characteristic point in described first image and second image It is the first ratio with the ratio of secondary little Euclidean distance to levy the minimum euclidean distance between vector；

First ratio be less than first threshold when, with described first image corresponding with the minimum euclidean distance in Characteristic point and second image in characteristic point be the 3rd matching double points, generate the with the 3rd matching double points as element One set；

Spy with the characteristic point in the characteristic vector of each characteristic point in second image and described first image It is the second ratio with the ratio of secondary little Euclidean distance to levy the minimum euclidean distance between vector；

Second ratio be less than Second Threshold when, with second image corresponding with the minimum euclidean distance in Characteristic point and described first image in characteristic point be the 4th matching double points, generate the with the 4th matching double points as element Two set；

Take the common factor of the first set and the second set to obtain described first image and second image Initial matching point pair.

Optionally, the characteristic point in the characteristic vector of each characteristic point in described first image and second image Characteristic vector between minimum euclidean distance refer to the ratio of secondary little Euclidean distance：Each feature in described first image The characteristic vector of point is preset less than first apart from its poor absolute value with the abscissa with this feature point in second image Minimum euclidean distance between the characteristic vector of distance and the characteristic point for belonging to same difference pyramid image layer with this feature point Ratio with secondary little Euclidean distance；

The feature of the characteristic point in the characteristic vector of each characteristic point in second image and described first image Minimum euclidean distance between vector is referred to the ratio of secondary little Euclidean distance：The spy of each characteristic point in second image Levy vector with described first image with the abscissa of this feature point apart from its poor absolute value less than the second predeterminable range and Minimum euclidean distance between the characteristic vector of the characteristic point for belonging to same difference pyramid image layer with this feature point and time little The ratio of Euclidean distance.

Optionally, the characteristic point in the characteristic vector of each characteristic point in described first image and second image Characteristic vector between minimum euclidean distance refer to the ratio of secondary little Euclidean distance：Each feature in described first image The characteristic vector of point is preset less than the 3rd apart from its poor absolute value with the vertical coordinate with this feature point in second image Minimum euclidean distance between the characteristic vector of distance and the characteristic point for belonging to same difference pyramid image layer with this feature point Ratio with secondary little Euclidean distance；

The feature of the characteristic point in the characteristic vector of each characteristic point in second image and described first image Minimum euclidean distance between vector is referred to the ratio of secondary little Euclidean distance：The spy of each characteristic point in second image Levy vector with described first image with the vertical coordinate of this feature point apart from its poor absolute value less than the 4th predeterminable range and Minimum euclidean distance between the characteristic vector of the characteristic point for belonging to same difference pyramid image layer with this feature point and time little The ratio of Euclidean distance.

Optionally, the matching double points for removing the initial matching point centering mistake are included with obtaining matching double points：

Slope of the initial matching point of described first image and second image to place line is calculated, with described initial The slope of matching double points place line is abscissa, and initial matching point corresponding with the slope is that vertical coordinate is generated tiltedly to number Rate rectangular histogram；

Determine initial matching point in the slope histogram to the initial matching point of number sum place cluster when maximum to for First matching double points；

As abscissa, corresponding with the difference of the first coordinate first mates difference with the first coordinate of first matching double points Point is that vertical coordinate generates the first rectangular histogram to number；

When determining that the first matching double points number sum is maximum in first rectangular histogram, the first matching double points of place cluster are Second matching double points；

As abscissa, corresponding with the difference of the second coordinate second mates difference with the second coordinate of second matching double points Point is that vertical coordinate generates the second rectangular histogram to number；

When determining that the second matching double points number sum is maximum in second rectangular histogram, the second matching double points of place cluster are Matching double points.

Optionally, the initial matching point for calculating described first image and second image in the following way connects to being located The slope of line：

Wherein：K_iFor slope, (x of the i-th pair initial matching point to place line_1i,y_1i)、(x_2i,y_2i) it is that i-th pair is initial Matching double points, (x_1i,y_1i) for i-th initial matching point in described first image position, (x_2i,y_2i) it is second figure The position of i-th initial matching point as in, W₁Width for described first image.

For solving the above problems, technical solution of the present invention also provides a kind of image acquiring method, including：

The matching double points that first image and the second image are determined using the method for above-mentioned determination matching double points；

Determined based on the position relationship between the matching double points inclined between described first image and second image Move；

Described first image and second figure are determined according to the skew between described first image and second image The overlapping region of picture；

Described first image and second image are spliced according to the overlapping region.

Compared with prior art, technical solution of the present invention has advantages below：

Pyramid decomposition is carried out to described image first, based on decomposition after image configuration difference pyramid diagram picture；Time then Each layer of the difference pyramid diagram picture is gone through, the extreme point that searches in every layer of difference pyramid diagram picture, the extreme point association The absolute value of the gray value of the pixel in default neighborhood；Finally remove in the extreme point as in described image strong edge Pixel is obtaining the characteristic point in described image.Per layer is searched for due to having traveled through each layer of the difference pyramid diagram picture Extreme point in difference pyramid diagram picture, determines the side of extreme point relative to existing with difference pyramid diagram picture place space For method, the complexity of detection is low, and then improves the speed of detection characteristic point to a certain extent, due to eliminating extreme point In for the pixel in described image strong edge, therefore stable characteristic point can be obtained.

Description of the drawings

Fig. 1 be the embodiment of the present invention one image in characteristic point detection method schematic flow sheet；

Fig. 2 is the schematic flow sheet of the method for the determination matching double points of the embodiment of the present invention two；

Fig. 3 is the sub- schematic diagram of feature point description of the embodiment of the present invention two；

Fig. 4 is the schematic flow sheet of the method for the determination matching double points of the embodiment of the present invention three；

Fig. 5 be the embodiment of the present invention three the first image of acquisition and the second image in initial matching point to the oblique of place line The schematic diagram of rate；

Fig. 6 is the schematic flow sheet of the image acquiring method of the embodiment of the present invention four；

Fig. 7-a to Fig. 7-d are the position relationship schematic diagrams between the first image and the second image.

Specific embodiment

Understandable for enabling the above objects, features and advantages of the present invention to become apparent from, below in conjunction with the accompanying drawings to the present invention Specific embodiment be described in detail.Elaborate detail in order to fully understanding the present invention in the following description.But It is that the present invention can be implemented different from alternate manner described here with multiple, those skilled in the art can be without prejudice to this Similar popularization is done in the case of invention intension.Therefore the present invention is not limited by following public specific embodiment.

As described in prior art, when the characteristic point in stitching image is treated is detected, existing detection Algorithm complex is high, and detection speed is slow, additionally, when the characteristic point to detecting is mated, the accuracy of matching double points Not high, therefore to be spliced based on the matching double points, the splicing precision of the image obtained after splicing is low, and clinical diagnosises are brought Affect.

Therefore, inventor propose existing feature point detecting method is improved, and based on improvement after characteristic point inspection Survey method further improves distinguished point based come the method for determining matching double points, improves the image split-joint method of feature based, To realize characteristic point in image is quickly detected with relatively low complexity, simple and quick accurately determine adjacent two width image Between matching double points, and then realize obtaining the splicing higher image of precision with speed faster.

Technical scheme is described in detail below by way of specific embodiment.

Embodiment one

Refer to Fig. 1, Fig. 1 be the embodiment of the present invention one image in characteristic point detection method schematic flow sheet；Such as Shown in Fig. 1, in the image of the present embodiment, the detection method of characteristic point includes：

S101：Pyramid decomposition is carried out to described image, based on decomposition after image configuration difference pyramid diagram picture；

S102：Each layer of the difference pyramid diagram picture is traveled through, the extreme point that searches in every layer of difference pyramid diagram picture, The extreme point is associated with the absolute value of the gray value of the pixel in default neighborhood；

S103：Remove is the pixel in described image strong edge in the extreme point obtaining the feature in described image Point.

S101 is executed, pyramid decomposition is carried out to described image, be that Gauss gold word is carried out to described image in the present embodiment Tower decomposes, and sets up the gaussian pyramid of described image in other words.And based on decomposition after image configuration difference of Gaussian pyramid diagram Picture.In the present embodiment, carrying out gaussian pyramid decomposition to described image can be carried out with described image by Gaussian function first Convolution, obtains the Gaussian smoothing image of different scale, then the Gaussian smoothing image of different scale is carried out down-sampled with composition Gaussian pyramid image.Specifically, in the present embodiment, gaussian pyramid image is obtained by equation below：

L (x, y, σ)=G (x, y, σ) * I (x, y)

Wherein, I (x, y) is input picture, and G (x, y, σ) is Gaussian function, and L (x, y, σ) is gaussian pyramid image, and σ is The yardstick of gaussian kernel, the coordinate of (x, y) for pixel.

For for Gaussian function G (x, y, σ), (x_i,y_i) for gaussian kernel center.In the present embodiment, with the gradually increase of σ, the Gaussian smoothing figure of different scale can be generated Picture, during σ gradually increases, carries out down-sampled Gaussian smoothing figure after down-sampled to the Gaussian smoothing image As constituting gaussian pyramid image.Can be controlled by the size of the value to σ in the present embodiment, to control the spy for detecting Quantity a little is levied, and then reduces the time of the detection characteristic point.Next, the Gaussian difference of the construction gaussian pyramid image Divide pyramid image, difference of Gaussian function D (x, y, σ)=[G (x, y, k σ)-G (x, y, σ)] * I (x, y)=G (x, y, k σ) * I (x,y)-G(x,y,σ)*I(x,y).

From the foregoing, G (x, y, σ) * I (x, y) is the image that described image is carried out to obtain after gaussian pyramid decomposition, That is L (x, y, σ).Therefore, the difference of Gaussian pyramid diagram picture of described image is the difference of the gaussian pyramid image of adjacent yardstick, I.e.：D (x, y, σ)=L (x, y, k σ)-L (x, y, σ).

The difference of Gaussian pyramid diagram picture that described image is obtained by S101, next executes S102, determines the height Extreme point in this difference pyramid diagram picture, specifically, exactly travels through each layer of the difference of Gaussian pyramid diagram picture, search Extreme point present in each layer of difference of Gaussian pyramid diagram picture, in the present embodiment, the extreme point is associated with default neighborhood In pixel gray value absolute value, in the present embodiment, the extreme point can be picture in every layer of difference pyramid diagram picture The absolute value of the gray value of vegetarian refreshments is the pixel of the maximum absolute value of the gray value of pixel in the default neighborhood of the pixel. The big I of the default neighborhood according to depending on actual demand (such as：The final intensity and characteristic point of characteristic point to be determined The size in shared region in described image), in the present embodiment, the default neighborhood can be eight centered on the pixel Neighborhood, namely the pixel array of 3 × 3 centered on the pixel, can also be 24 centered on the pixel Neighborhood, namely the pixel array of 5 × 5 centered on the pixel.For example, it is exactly the Gaussian difference parting to each layer For word tower image, by the absolute value of the gray value of each pixel in this layer and the pixel of predetermined neighborhood around which The absolute value of gray value is compared, when the pixel gray value absolute value than which around all pixels point gray value Absolute value all big when, then the pixel is the extreme point in this layer of difference of Gaussian pyramid diagram picture.

In the present embodiment, the accuracy in order to improve the extreme point for searching further is improved in the image for eventually detecting The accuracy of characteristic point, the extreme point can also be revised pixel P, and the revised pixel P is to pixel The position of point P is obtained after being modified, and the pixel P refers to the gray value of pixel in every layer of difference pyramid diagram picture Absolute value is the pixel of the maximum absolute value of the gray value of pixel in the default neighborhood of the pixel.Pixel P herein Acquisition be also by will be around the absolute value of the gray value of each pixel in every layer of difference pyramid diagram picture and its in advance The absolute value for determining the gray value of the pixel of neighborhood is compared, when the pixel gray value absolute value than which around institute When having the absolute value of the gray value of pixel all big, the pixel is pixel P.After the pixel P is obtained, to its position It is modified to obtain revised pixel P, in the following way the position of the pixel P is repaiied in the present embodiment Just：

First, side-play amount is first obtained, and the offset delta is obtained in the following way：

Wherein：D be difference of Gaussian function, X=(Δ x, Δ y, Δ σ)^T, Δ x, Δ y, Δ σ are respectively the x of extreme point and sit The x of mark, y-coordinate and the σ and pixel P_pCoordinate, y_pCoordinate and σ_pDifference.

After above-mentioned formula solves δ, by the x of the pixel P_pCoordinate, y_pCoordinate and σ_pPlus right with which in δ The vector that answers, then can obtain the position of revised pixel P, namely the position of extreme point.

In the present embodiment, the time of algorithm performs during in order to control to detect characteristic point, characteristic point in detection image is improved Speed, the extreme point can also be that the absolute value of the gray value to pixel P is located at after sorting according to order from large to small The pixel P of top N, the pixel P refer to that the absolute value of the gray value of pixel in every layer of difference pyramid diagram picture is institute State the pixel of the maximum absolute value of the gray value of pixel in the default neighborhood of pixel.Similarly, pixel P herein is obtained Must be also to pass through to make a reservation for neighbour around the absolute value of the gray value of each pixel in every layer of difference pyramid diagram picture and its The absolute value of the gray value of the pixel in domain is compared, when the pixel gray value absolute value than which around all pictures When the absolute value of the gray value of vegetarian refreshments is all big, the pixel is pixel P.In the present embodiment, the value of N is empirical value, passes through The time of algorithm performs during the accuracy rate of the characteristic point detected in multiple image and detection characteristic point is traded off with Final acquisition N values, in the present embodiment, the span of N can be 30～50, and such as N can take 40, the ash of the pixel P to obtaining The absolute value of angle value is ranked up according to descending order, and the absolute value of the gray value of capture vegetarian refreshments P is located at first 40 Pixel P removes the weaker pixel of other features in pixel P as extreme point.

Additionally, in the present embodiment when extreme point is determined, or first to the absolute value of the gray value of pixel P according to Order from large to small is ranked up, and the absolute value for first taking out the gray value of pixel P is located at the pixel P of top N, then right The position for being located at the pixel P of top N is modified, and the absolute value of the gray value of pixel P is located at top N and to this finally The position of N number of pixel P be modified after pixel P as extreme point, extreme point is determined using which, final is obtained The accuracy of extreme point is high, and can improve the speed of characteristic point in subsequent detection image.

The extreme point obtained by S102, if as characteristic point, can wherein include unstable characteristic point, therefore need Unstable characteristic point is gone the characteristic point stable divided by acquisition.Therefore, it is the figure to execute S013 and remove in the extreme point As the pixel in strong edge is obtaining stable characteristic point in described image.Due to the pixel in image strong edge across There is larger principal curvatures in the direction at edge, and the direction principal curvatures along vertical edge is then less, therefore can by principal curvatures come Judge that those pixels in extreme point are the pixels in described image strong edge.Further, since the size of principal curvatures and The eigenvalue of Hessian matrixes is proportional, so in the present embodiment, can pass through the eigenvalue for calculating Hessian matrixes To determine principal curvatures, and then determine the pixel being located in described image strong edge.

In the present embodiment, specifically, by calculating maximum principal curvatures and minimum master of the extreme point in its predetermined field Curvature, and determine that according to the ratio of the two whether the extreme point is the pixel in described image strong edge.From the foregoing, The eigenvalue of principal curvatures and Hessian matrixes is directly proportional, and therefore the ratio of maximum principal curvatures and minimum principal curvatures can pass through meter The ratio of eigenvalue of maximum and minimal eigenvalue of Hessian matrixes is calculated obtaining.In the present embodiment, Hessian matrixes are as follows Shown：

Wherein：D is difference of Gaussian function

D_xx=D (y, x+1, σ)+D (y, x-1, σ) -2 × D (x, y, σ)

D_yy=D (y+1, x, σ)+D (y-1, x, σ) -2 × D (x, y, σ)

D_xy=0.25 × [D (y+1, x+1, σ)+D (y-1, x-1, σ)-D (y+1, x-1, σ)-D (y-1, x+1, σ)]

(x, y) is the coordinate of the extreme point, calculates the eigenvalue of maximum and minimal eigenvalue of above-mentioned Hessian matrixes Between ratio, then the ratio is compared with default threshold value, the default threshold value rule of thumb depending on, its close The order of accuarcy for rejecting pixel in image strong edge is coupled to, and remaining pixel quantity executes the shadow of time to subsequent algorithm Ring.Default threshold value described in the present embodiment may range from 1～30, such as：The default threshold value can be 20.By above-mentioned Formula is could be aware that when the extreme point in the maximum principal curvatures of its 2 × 2 neighborhood and the ratio of minimum principal curvatures more than described During default threshold value, the extreme point is the pixel in described image strong edge.By to the extreme point in its 2 × 2 neighborhood Maximum principal curvatures and minimum principal curvatures ratio judgement, can be by the figure present in the extreme point searched in S102 As the pixel in strong edge is removed, the more stable characteristic point of final acquisition.

So far, the detection to characteristic point in image is achieved by above-mentioned S01～S103, due to having traveled through the difference Each layer of pyramid diagram picture searching for the extreme point in every layer of difference pyramid diagram picture, relative to existing with difference pyramid Image place space is come, for determining the method for extreme point, the complexity of detection is low, and then improves detection to a certain extent The speed of characteristic point, due to eliminating in extreme point as the pixel in described image strong edge, therefore can obtain stable spy Levy a little.

Furthermore, it is necessary to illustrate, in actual applications to image in characteristic point when detecting, should according to different With scene, feature point detection can be carried out to entire image, it is also possible to feature point detection is carried out just for parts of images, for example： If being to be applied to subsequently spell the first image and the second image after carrying out feature point detection to the first image and the second image Connect, then when feature point detection is carried out to the first image and the second image, can be just for the first image and the second image substantially The region that the region (initial overlapping region) of overlap is located in the first image carries out feature point detection, the first image and the second figure The region that the initial overlapping region of picture is located in the second image carries out feature point detection.

Corresponding to the detection method of characteristic point in above-mentioned image, the present embodiment also provides a kind of inspection of characteristic point in image Device is surveyed, the detection means of characteristic point includes in described image：

Resolving cell, for carrying out pyramid decomposition to described image, based on decomposition after image configuration difference pyramid Image；

Search unit, for traveling through each layer of the difference pyramid diagram picture, in every layer of difference pyramid diagram picture of search Extreme point, the extreme point is associated with the absolute value of the gray value of the pixel in default neighborhood；

First removal unit, described to obtain for the pixel in described image strong edge in the extreme point for removing Characteristic point in image.

The enforcement of the detection method for being embodied as referring to the characteristic point of the detection means of characteristic point in described image, Will not be described here.

The present embodiment also provides a kind of X-ray shooting system, including the detection means of characteristic point in above-mentioned image.

Embodiment two

The present embodiment provides a kind of method for determining matching double points, refers to Fig. 2, Fig. 2 be the embodiment of the present invention two really Determine the schematic flow sheet of the method for matching double points, as shown in Fig. 2 the method for determining matching double points includes：

S101′：Pyramid decomposition is carried out to the first image and the second image, based on decomposition after the first image and the second figure As construct respectively corresponding with described first image the first difference pyramid diagram picture and with second image corresponding second Difference pyramid diagram picture；Described first image and second image are adjacent image；

S102′：Each layer of the first difference pyramid diagram picture is traveled through, the pole that searches in every layer of difference pyramid diagram picture Value point, travels through each layer of the second difference pyramid diagram picture, and the extreme point that searches in every layer of difference pyramid diagram picture is described Extreme point is associated with the absolute value of the gray value of the pixel in default neighborhood；

S103′：Remove in the extreme point of the first difference pyramid diagram picture as the picture in described first image strong edge Vegetarian refreshments is obtaining the characteristic point in described first image；It is described in the extreme point for removing the second difference pyramid diagram picture Pixel in two image strong edges is obtaining the characteristic point in second image；

S104：Characteristic point in described first image and second image is mated to generate initial matching point Right；

S105：Difference with the first coordinate of the initial matching point pair is as abscissa, corresponding with the difference of the first coordinate first Beginning matching double points number is that vertical coordinate generates the first rectangular histogram, determines that initial matching point is to number sum in first rectangular histogram When maximum, the initial matching point of place cluster is to for the first matching double points；

S106：Difference with the second coordinate of first matching double points as abscissa, corresponding with the difference of the second coordinate One matching double points number is that vertical coordinate generates the second rectangular histogram, determines the first matching double points number sum in second rectangular histogram When maximum, the first matching double points of place cluster are matching double points.

In the present embodiment, S101 '～S103 ' detects the characteristic point in the second image of the characteristic point in the first image and detection Similar with the S101～S103 in embodiment one, here is omitted, is detecting described first image and second figure After characteristic point as in, S104 is executed, the characteristic point in the two is mated to generate initial matching point pair, specifically, this In the following way the characteristic point in described first image and the second image is mated in embodiment：

First, the characteristic vector of described first image and the characteristic point in second image is obtained, for characteristic point For characteristic vector, the information typically by the pixel near this feature point is retouching to the characteristic vector of this feature point State, be the gradient of the pixel of predetermined neighborhood with the characteristic point in the first image, gradient magnitude, gradient direction in the present embodiment To be described to the characteristic point in the first image, namely the characteristic vector of the characteristic point in described first image is predetermined by which The gradient of the pixel of neighborhood, gradient magnitude and gradient vector are characterizing.

Hereinafter, by taking described first image as an example, the acquisition of the characteristic vector of the characteristic point in the first image is carried out accordingly Explanation.With the characteristic point as origin in the present embodiment, formed centered on the characteristic point for radius by predetermined length Predetermined neighborhood, in the present embodiment, the radius radius can be obtained by equation below：

Wherein：σ_octThe yardstick of the gaussian kernel of the difference pyramid diagram picture being located for the characteristic point, d is variable, its value Be associated with order of accuarcy and algorithm execution time that the characteristic vector of generation is described to feature vertex neighborhood, d may range from 2～ 8, d=4 in the present embodiment.After the predetermined neighborhood centered on the characteristic point is determined, a certain in difference pyramid diagram picture Gradient gradI (x, y) of pixel of the characteristic point of layer in the predetermined neighborhood, gradient magnitude m (x, y), gradient direction θ (x, y) is obtained by equation below respectively：

Wherein, I (x, y) be input picture, gradI (x, y) be gradient, m (x, y) be gradient magnitude, θ (x, y) be gradient Direction, the coordinate that (x, y) is pixel.

After calculating gradient, gradient magnitude and the gradient direction of the pixel of the predetermined neighborhood of each characteristic point, to pre- The gradient direction and gradient magnitude for determining the pixel of neighborhood carries out statistics with histogram, to obtain the characteristic vector of this feature point.This In embodiment, (each unit represents a pixel, and characteristic point is 8 × 8 for unit with the predetermined neighborhood of characteristic point as 8 × 8 Unit center), namely predetermined neighborhood is to illustrate as a example by 64 pixels to obtain with this feature corresponding characteristic vector of point ?.Refer to Fig. 3, Fig. 3 is the sub- schematic diagram of feature point description of the embodiment of the present invention two, and left side show 64 pixels in Fig. 3 Point (represented with 64 cells), the gradient direction and gradient magnitude of each pixel by Fig. 3 shown in left side with side Represent to the small arrow with size, in order to carry out statistics with histogram to the gradient direction and gradient magnitude of 64 pixels, first by 8 × 8 cell is divided into the cell of 44 × 4, the gradient direction and gradient width to the pixel in 44 × 4 cells Value carries out statistics with histogram, in the present embodiment for each 4 × 4 cell, draw respectively from all directions to Gradient distribution Rectangular histogram, that is to say, that for 16 pixels in each 4 × 4 cell, according to certain rule come to this 16 The gradient direction and gradient magnitude of pixel carries out statistics with histogram, such as：By the gradient direction of pixel finally statistics 0 °, On 45 °, 90 °, 135 °, 180 °, 225 °, 270 °, 315 ° and 360 ° this 8 directions, and why the gradient direction of pixel is when being worth The gradient direction is classified as one of above-mentioned 8 directions, then can be according to predetermined rule depending on, such as：Gradient side when pixel To during more than 0 ° less than 45 °, the gradient direction of the pixel is classified as 45 °, when the gradient direction of pixel is less than more than 45 ° When 90 °, the gradient direction of the pixel is classified as 90 ° etc., the gradient direction of pixel is being classified as one of above-mentioned 8 directions Afterwards, the length of arrow has then corresponded to the superposition of different pixels point gradient magnitude in this direction in this direction, by this 44 × 4 Cell as seed point, shown in Figure 3, in Fig. 3 right side unit center of a lattice (round dot of black) be characterized a little, be The distance between embodiment different pixels point and characteristic point, enclose Gauss weight to the gradient magnitude of each pixel, and then one Individual characteristic point defines 8 × 4=32 dimensional vectors, so as to obtain 32 dimension description of characteristic point：W=(w₁, w₂... w₃₂), in addition For mitigating the impact that different characteristic point is caused to peripheral region because of illumination difference, it is standardized to describing son, in the present embodiment 32 above-mentioned dimension description are standardized by equation below：

And then obtain the characteristic vector of the later characteristic point of standardization：L=(l₁, l₂... l₃₂).So far pre- by characteristic point Gradient, gradient vector and the gradient magnitude for determining the pixel of neighborhood is described to the characteristic point, obtains the feature The characteristic vector of point.

For similarly, for the characteristic point in second image, it is also adopted by above-mentioned method and obtains second figure The characteristic vector of the characteristic point as in.Next based on described first image in characteristic point characteristic vector and second figure The characteristic vector of the characteristic point as in, carries out bi-directional matching with life to the characteristic point in described first image and second image Into initial matching point pair.

Specifically, in the following way the characteristic point in described first image and the second image is carried out in the present embodiment double To coupling, first：Calculate the characteristic point in characteristic vector and second image of each characteristic point in described first image Characteristic vector between European geometric distance, for example, the set that the characteristic vector of the characteristic point in the first image is located For { a₁、a₂、a₃…a_n(in set, each characteristic vector is 32 dimensions), the characteristic vector of the characteristic point in the second image is located Set { b₁、b₂、b₃…b_n(in set, each characteristic vector is 32 dimensions), then calculate a respectively₁And b₁、a₁And b₂、…a₁With b_nBetween European geometric distance, a₂And b₁、a₂And b₂、…a₂And b_nBetween European geometric distance, a_nAnd b₁、a_nAnd b₂、…a_n And b_nBetween European geometric distance.For a₁For itself and b₁、b₂、b₃…b_nBetween European geometric distance in, have one Minimum European geometric distance and one little European geometric distance, calculate the first ratio f therebetween₁, similarly for a₂And Say itself and b₁、b₂、b₃…b_nBetween European geometric distance in, there is also the European geometric distance of minimum and one little Ou Formula geometric distance, calculates the first ratio f therebetween₂, the rest may be inferred obtains the spy of each characteristic point in the first image Levy the minimum European geometric distance and time little European geometric distance between the characteristic vector of the characteristic point in vector and the second image The first ratio, i.e. f₁、f₂、f₃、f₄…f_n.If f₁、f₂、f₃、f₄…f_nMiddle exist less than first threshold the first ratio, then with The characteristic point in characteristic point and second image in described first image corresponding with the minimum European geometric distance is 3rd matching double points, for example, if f₄The first threshold is less than, with f₄Corresponding minimum European geometric distance be characterized to Amount a₄With characteristic vector b₁₂Between European geometric distance, then in the characteristic point 4 in described first image and second image Characteristic point 12 be the 3rd matching double points.For all the first ratios less than the first threshold, corresponding characteristic point is found out To obtain the 3rd matching double points, all of 3rd matching double points constitute first set.In the present embodiment, the first threshold For empirical value, the accuracy of the 3rd matching double points when being associated with coupling, first threshold described in the present embodiment may range from 0.1～10, if the first threshold can be 6.

Then, similarly calculate in second image in the characteristic vector of each characteristic point and described first image Euclidean distance between the characteristic vector of characteristic point, i.e., calculate b respectively₁And a₁、b₁And a₂、…b₁And a_nBetween European geometry away from From b₂And a₁、b₂And a₂、…b₂And a_nBetween European geometric distance, b_nAnd a₁、b_nAnd a₂、…b_nAnd a_nBetween European geometry Distance.For b₁For itself and a₁、a₂、a₃…a_nBetween European geometric distance in, there is also the European geometric distance of minimum With one little European geometric distance, the second ratio s therebetween is calculated₁, the rest may be inferred obtain each in the second image Minimum European geometric distance between the characteristic vector of the characteristic point in the characteristic vector of individual characteristic point and the first image and time little Second ratio of European geometric distance, i.e. s₁、s₂、s₃、s₄…s_n.If s₁、s₂、s₃、s₄…s_nMiddle exist less than Second Threshold the Two ratios, then with the characteristic point and described first image in second image corresponding with the minimum European geometric distance in Characteristic point be the 4th matching double points, for example, if s₄The Second Threshold is less than, with s₄Corresponding minimum European geometry away from From being characterized vectorial b₄With characteristic vector a₁₂Between European geometric distance, then with the characteristic point 4 in second image and institute It is the 4th matching double points to state the characteristic point 12 in the first image.For all the second ratios less than the Second Threshold, find out To obtain the 4th matching double points, all of 4th matching double points constitute second set to corresponding feature point pairs.In the present embodiment, The Second Threshold be empirical value, the accuracy of the 4th matching double points when being associated with coupling, Second Threshold described in the present embodiment May range from 0.1～10, as the Second Threshold can be 5.

Common factor is taken to above-mentioned first set and second set, the matching double points in common factor are described first image and described The initial matching point pair of the second image.By way of bi-directional matching, the matching degree between matching double points can be improved, therefore The matching degree of the final initial matching point pair for obtaining is preferable.

In the present embodiment, when carrying out bi-directional matching, for the characteristic vector of each characteristic point in the first image During European geometric distance between the characteristic vector of the characteristic point in which is calculated with the second image, traveled through in the second image The characteristic vector of all of characteristic point being calculated, similarly, for the feature of each characteristic point in the second image During European geometric distance between the characteristic vector of the characteristic point for vector in which is calculated with the first image, it is to have traveled through the In one image, the characteristic vector of all of characteristic point is being calculated.Consider actual application scenarios, described first image And second image between may in the horizontal direction overall offset larger, skew in vertical direction is very little or does not deposit ?；Or may be larger in vertical direction overall offset between described first image and second image, in the horizontal direction partially Move very little or do not exist.Such as：For the first image and the second image that X-ray shooting system is collected, person under inspection It is parallel between place plane and detector place plane, the amplification difference between the first image and the second image can be neglected Slightly disregard, therefore, between the first image and the second image, only exist the translation relation on position, i.e. the first image and the second image Only exist in the horizontal direction with vertical direction on skew, when gathering image using X-ray shooting system, detector and bulb can To move (also commonly referred to as detector and bulb are vertically moved) along column place direction, such as：Vertical position shoots, and also may be used To move (also commonly referred to as detector and bulb are moved in the horizontal direction) along level bed X-axis place direction, such as：Clinostatism shoots. If detector and bulb are moved along column place direction or level bed X-axis (major axis) place direction, it is located when detector is moved and leads The precision of rail is sufficiently high, detector during moving along column place direction perpendicular direction movement less or Movement is there is no, detector is less in perpendicular direction movement during moving along level bed X-axis place direction Or there is no movement, then the first image and the second image the direction vertical with column place direction or with level bed The skew in the vertical direction in X-axis place direction is smaller or is substantially absent from, namely between the first image and the second image Skew only in a direction is larger.

It should be noted that stand position and shoot for detector and bulb are moved along column place direction in the present embodiment, With column place direction as vertical direction (vertical coordinate), the direction vertical with column place direction is entered for horizontal direction (abscissa) Row explanation, the abscissa and vertical coordinate of the image that corresponding vertical position is collected when shooting are corresponding with the direction of foregoing description.Right Moved in detector and bulb in the horizontal direction, i.e., clinostatism shoots, with level bed X-axis place direction as horizontal direction (abscissa), The direction vertical with level bed X-axis place direction be vertical direction (vertical coordinate), the image collected when being shot with clinostatism accordingly Abscissa and vertical coordinate corresponding with the direction which describes.

In the present embodiment, if between described first image and second image may in the horizontal direction (abscissa) overall Skew is larger (for example：Clinostatism shoots), the skew in vertical direction is very little or does not exist；Now first figure is being calculated Minimum European between the characteristic vector of the characteristic point in the characteristic vector and second image of each characteristic point as in During the ratio of distance and secondary little Euclidean distance, only can calculate the characteristic vector of each characteristic point in described first image with It is less than the spy of the characteristic point of the first predeterminable range in second image with the vertical coordinate of this feature point apart from its poor absolute value Levy the ratio of the minimum euclidean distance and time little Euclidean distance between vector；First predeterminable range be associated with matching accuracy rate and The practical situation of the compromise and different system of algorithm execution time, first predeterminable range may range from 1～500 Pixel, if first predeterminable range can be 300 pixels.Each characteristic point in second image is calculated Characteristic vector and described first image in characteristic point characteristic vector between minimum euclidean distance and secondary little Euclidean distance Ratio when, only can calculate the characteristic vector of each characteristic point in second image with described first image with should The vertical coordinate of characteristic point apart from its difference absolute value less than the second predeterminable range characteristic point characteristic vector between minimum Europe Formula distance and the ratio of time little Euclidean distance；Second predeterminable range is associated with the compromise of matching accuracy rate and algorithm execution time And the practical situation of different system, second predeterminable range may range from 1～500 pixel, and such as described second Predeterminable range can be 300 pixels.For example, if the coordinate of the characteristic point 4 in the first image is (x₄, y₄), the second figure The coordinate of the characteristic point 12 as in is (x₁₂, y₁₂), the coordinate of characteristic point 21 is (x₂₁, y₂₁), | y₁₂-y₄|、|y₂₁-y₄| respectively less than First predeterminable range, then only calculate in the first image in the characteristic vector of characteristic point 4 and the second image characteristic point 12 and The ratio of minimum European geometric distance and time little European geometric distance between the characteristic vector of characteristic point 21.

If may be in the larger (example of vertical direction (vertical coordinate) overall offset between described first image and second image Such as：Vertical position shoots), offset very little in the horizontal direction or do not exist；Now calculate described first image in each Minimum euclidean distance between the characteristic vector of the characteristic point in the characteristic vector of characteristic point and second image and secondary little Ou During the ratio of formula distance, only can calculate the characteristic vector of each characteristic point in described first image with second image In with the abscissa of this feature point apart from its poor absolute value less than between the characteristic vector of the characteristic point of the 3rd predeterminable range Minimum euclidean distance and the ratio of time little Euclidean distance；3rd predeterminable range is associated with matching accuracy rate and algorithm execution time Compromise and different system practical situation, the 3rd predeterminable range may range from 1～500 pixel, this reality It can be 100 pixels to apply the 3rd predeterminable range described in example.Each characteristic point in second image is calculated Minimum euclidean distance between the characteristic vector of the characteristic point in characteristic vector and described first image and secondary little Euclidean distance Than when, only can calculate the characteristic vector of each characteristic point in second image with described first image with the spy Levy minimum European between characteristic vector of the absolute value of abscissa a little apart from its difference less than the characteristic point of the 4th predeterminable range Distance and the ratio of time little Euclidean distance；4th predeterminable range be associated with matching accuracy rate and algorithm execution time compromise with And the practical situation of different system, the 4th predeterminable range may range from 1～500 pixel, institute in the present embodiment It can be 400 pixels to state the 4th predeterminable range.For example, if the coordinate of the characteristic point 4 in the first image is (x₄, y₄), The coordinate of the characteristic point 12 in the second image is (x₁₂, y₁₂), the coordinate of characteristic point 21 is (x₂₁, y₂₁), | x₁₂-x₄|、|x₂₁-x₄| Respectively less than described 3rd predeterminable range, then only calculate in the first image characteristic point in the characteristic vector of characteristic point 4 and the second image 12 and characteristic point 21 characteristic vector between minimum European geometric distance and time little European geometric distance ratio.

Additionally, for above-mentioned X-ray shooting system, due to putting down between person under inspection place plane and detector place plane OK, the amplification difference between the first image and the second image is negligible, now in described first image is calculated Minimum euclidean distance between the characteristic vector of the characteristic point in the characteristic vector of each characteristic point and second image with During the ratio of secondary little Euclidean distance, only can calculate the characteristic vector of each characteristic point in described first image with described the Between the characteristic vector of the characteristic point for belonging to same difference pyramid image layer in two images with this feature point minimum European away from From the ratio with secondary little Euclidean distance.For example, the characteristic point 4 in described first image the in difference pyramid diagram picture the 12nd Layer, and therefore the characteristic point 1,3,6,12,21 in second image only can be counted also at the 12nd layer of difference pyramid diagram picture Calculate the feature of the characteristic point 1,3,6,12,21 in characteristic vector and second image of the characteristic point 4 in described first image The ratio of minimum European geometric distance and time little European geometric distance between vector.And it is each in second image is calculated Minimum euclidean distance between the characteristic vector of the characteristic point in the characteristic vector and described first image of individual characteristic point is little with secondary During the ratio of Euclidean distance, only can calculate the characteristic vector of each characteristic point in second image with first figure As in belong to same difference pyramid image layer with this feature point characteristic point characteristic vector between minimum euclidean distance with The ratio of secondary little Euclidean distance.

In addition, from the above for the image of X-ray shooting system collection, may between the first image and the second image There is the skew of horizontal direction or vertical direction and amplification difference therebetween is negligible, therefore to described first When characteristic point in image and the second image carries out bi-directional matching, between described first image and second image in water Square larger to overall offset, skew in vertical direction is very little or does not exist (for example：Clinostatism shoots), the present embodiment exists Calculate the characteristic vector of the characteristic point in characteristic vector and second image of each characteristic point in described first image Between minimum euclidean distance and secondary little Euclidean distance ratio when, can also only calculate each feature in described first image The characteristic vector of point is preset less than first apart from its poor absolute value with the vertical coordinate with this feature point in second image Minimum euclidean distance between the characteristic vector of distance and the characteristic point for belonging to same difference pyramid image layer with this feature point Ratio with secondary little Euclidean distance；The characteristic vector and described first image of each characteristic point in second image is calculated In characteristic point characteristic vector between minimum euclidean distance and secondary little Euclidean distance ratio when, can also only calculate described The characteristic vector of each characteristic point in two images with poor apart from it with the vertical coordinate of this feature point in described first image Absolute value less than the second predeterminable range and with this feature point belong to the feature of the characteristic point of same difference pyramid image layer to The ratio of minimum euclidean distance and time little Euclidean distance between amount.

Similarly, when the characteristic point in described first image and the second image carries out bi-directional matching, for vertical position is clapped When taking the photograph, larger in vertical direction overall offset between described first image and second image, offset in the horizontal direction very Little or do not exist；The characteristic vector of each characteristic point of the present embodiment in described first image is calculated and second figure During the ratio of minimum euclidean distance and secondary little Euclidean distance between the characteristic vector of the characteristic point as in, only can also calculate described The characteristic vector of each characteristic point in the first image with second image with the abscissa of this feature point apart from it Poor absolute value is less than the 3rd predeterminable range and is belonged to the feature of the characteristic point of same difference pyramid image layer with this feature point The ratio of minimum euclidean distance and time little Euclidean distance between vector；Each characteristic point in second image is calculated Minimum euclidean distance between the characteristic vector of the characteristic point in characteristic vector and described first image and secondary little Euclidean distance Than when, only can also calculate the characteristic vector of each characteristic point in second image with described first image with should The abscissa of characteristic point is less than the 4th predeterminable range apart from the absolute value of its difference and belongs to same difference pyramid with this feature point The ratio of minimum euclidean distance and time little Euclidean distance between the characteristic vector of the characteristic point of image layer.

In the present embodiment, when the characteristic point in described first image and second image carries out bi-directional matching, root The feature in the characteristic vector of characteristic point in described first image and second image is calculated according to different application scenarios Minimum European geometric distance and time little European geometric distance between the characteristic vector of point, can improve the speed of Feature Points Matching Degree, and then the speed for determining matching double points is also improved, while the accurate of the initial matching point pair that coupling is obtained can also be improved Degree.

So far above-mentioned steps are passed through, it is achieved that the described first image detected by S101 '～S103 ' and second figure The coupling of the characteristic point as in, obtains initial matching point pair.But for the initial matching point of coupling acquisition is for, wherein Can there are the matching double points of mistake, accordingly, it would be desirable to the matching double points of the initial matching point centering mistake are removed, in the present embodiment The matching double points of the initial matching point centering mistake are removed especially by S105～S106.

In the present embodiment, the angle of the position relationship between the first image and the second image is accounted for, and removes institute State the initial matching double points with point centering mistake that may be present.Consider between the first image and the second image in the horizontal direction May there is skew, the first image for such as collecting and the second image using above-mentioned X-ray shooting system with vertical direction, Therebetween only exist the translation relation on position, i.e. the first image and the second image is only existed in the horizontal direction and vertical direction On skew, therefore the initial matching point pair can be removed according to actual position relationship between the first image and the second image The middle wrong matching double points matching double points high to obtain accuracy.

From the foregoing, for different shooting positions, between the first image and the second image in the horizontal direction and vertically The skew in direction is different, and for vertical position shoots, detector and bulb are vertically moved, and guide rail can only ensure the One image and the second image are not in larger skew on level (abscissa) direction, and in vertical (vertical coordinate) direction On, even if on the premise of feature point detection and characteristic vector coupling are relatively accurate, calculating all initial matching points pair for obtaining The difference of vertical coordinate can be bigger than the difference of the abscissa of all initial matching points pair, therefore, the real coupling of initial matching point centering Difference and the matching double points of mistake of the point to the vertical coordinate of (the final matching double points for determining or referred to as correct matching double points) The difference of the difference of vertical coordinate also can be larger.And when detector and bulb are moved in the horizontal direction, guide rail can only ensure to treat first Image and the second image are not in larger skew on vertical (vertical coordinate) direction, and on level (abscissa) direction, Even if on the premise of feature point detection and characteristic vector coupling are relatively accurate, calculating the horizontal stroke of all initial matching points pair for obtaining The difference of coordinate can be bigger than the difference of the vertical coordinate of all initial matching points pair, therefore, the real matching double points of initial matching point centering The horizontal seat of the matching double points of the difference and mistake of the abscissa of (the final matching double points for determining or referred to as correct matching double points) The difference of the difference of mark also can be larger.

Therefore, for detector and bulb are vertically moving, to the feature in the first image and the second image Point is mated, and the difference of the vertical coordinate of the initial matching point pair of generation can bigger than the difference of abscissa (skew of vertical direction be more than The skew of horizontal direction).And for detector and bulb are moved in the horizontal direction, to the first image and the second image in Characteristic point is mated, and the difference of the abscissa of the initial matching point pair of generation can be than (the skew of horizontal direction greatly of the difference of vertical coordinate The skew of vertical direction can be more than).Therefore, in the initial matching point pair for obtaining described first image and the second image in the present embodiment Afterwards, can be by way of statistics with histogram respectively from the corresponding initial matching point of the difference that counts more close vertical coordinate to individual Number, the corresponding initial matching point of the difference of more close abscissa to number two in terms of carry out, can to remove initial matching point centering The matching double points of the mistake that can also exist.

Execute S105:Difference with the first coordinate of the initial matching point pair is as abscissa, corresponding with the difference of the first coordinate Initial matching point to number be vertical coordinate generate the first rectangular histogram, determine that initial matching point is to number in first rectangular histogram When sum is maximum, the initial matching point of place cluster is to for the first matching double points；In the present embodiment, if vertical position shoots, then the first figure Less, the skew in vertical direction is larger for skew between picture and the second image in the horizontal direction, therefore, initial matching point centering The difference of the difference of the vertical coordinate of the matching double points of the difference and mistake of the vertical coordinate of correct matching double points also can be larger, so, right For Yu Liwei shoots, first can carry out statistics with histogram to the difference of the vertical coordinate of the initial matching point pair to obtain first Right with point, be exactly specifically with the difference of the vertical coordinate of the initial matching point pair as abscissa, corresponding with the difference of vertical coordinate at the beginning of Beginning matching double points number is that vertical coordinate generates the first rectangular histogram, determines that initial matching point is to number sum in first rectangular histogram When maximum, the initial matching point of place cluster is to for the first matching double points.If clinostatism shoots, then the first image and the second image it Between vertical direction skew less, offset in the horizontal direction larger, therefore, the correct matching double points of initial matching point centering The difference of the difference of the abscissa of the matching double points of the difference of abscissa and mistake also can be larger, so for clinostatism shoots, can First to carry out statistics with histogram to obtain the first matching double points to the difference of the abscissa of the initial matching point pair, it is exactly specifically As abscissa, initial matching point corresponding with the difference of abscissa is vertical to number to difference with the abscissa of the initial matching point pair The first rectangular histogram of Coordinate generation, determine initial matching point in first rectangular histogram to number sum when maximum place cluster initial Matching double points are the first matching double points.

So that vertical position shoots as an example, the first image and the second image are respectively I₁And I₂, to I₁And I₂Characteristic point inspection is carried out Survey and coupling is to obtain initial matching point to rear, if belonging to I in initial matching point pair₁Pixel coordinate be (x_1i,y_1i)、 Belong to I₂Pixel coordinate be (x_2i,y_2i), (number of i ∈ [1, N], N for initial matching point pair), with y_2i-y_1iFor horizontal seat Mark, and to y_2i-y_1iThe number of corresponding initial matching point pair is counted, such as：Difference when the vertical coordinate of initial matching double points For 6 pixel units when, the number of corresponding initial matching point pair is 20, according to the statistical information, generates with initial With point to the difference of vertical coordinate be abscissa, the number of initial matching point pair corresponding with the difference of the vertical coordinate for vertical coordinate the One rectangular histogram, by first rectangular histogram to initial matching point to corresponding initial matching point under the difference of different vertical coordinates Statistics to number, can remove the initial matching point pair of mistake, this is because for the first figure for mating acquisition by S104 For, correct matching double points still account for major part to the initial matching point of picture and the second image, therefore, straight described first When the initial matching point included in a cluster in square figure is most to number, the initial matching point pair included by its cluster being located Probability for correct matching double points is maximum, therefore, retains in first rectangular histogram, and initial matching point to number sum is Included initial matching point pair in its place cluster when maximum, using included initial matching point in other clusters to as mistake Match point is removed, and using the initial matching point for retaining to as the first matching double points.

For shooting similarly, for clinostatism, by initial of first image and the second image of S104 coupling acquisitions With a centering, correct matching double points still account for major part, therefore, with the difference of the abscissa of the initial matching point pair be Abscissa, initial matching point corresponding with the difference of abscissa generate institute in a cluster in the first rectangular histogram to number for vertical coordinate Comprising initial matching point most to number when, the initial matching point that included of cluster which is located is to for correct matching double points Probability is maximum, therefore retains in first rectangular histogram, and initial matching point is to number sum by wrapping in its place cluster during maximum The initial matching point pair for including, included initial matching point in other clusters is removed to the match point as mistake, and will be retained Initial matching point to as the first matching double points.

In the present embodiment, the cluster can based on experience value depending on can also be obtained by experiment, namely described generating Before first rectangular histogram, need based on experience value or test the value for obtaining and the width of cluster is defined.In the present embodiment, described The width of cluster can be between 4～6 pixels.

The vertical seat of the most initial matching point pair that the difference for eliminating vertical coordinate by execution S105 is not belonging to calculate The part of the difference of mark, or the difference of the abscissa difference of the abscissa of most initial matching point pair that is not belonging to calculate Part, namely eliminate the matching double points of the initial matching point centering mistake to obtain the first matching double points.In the present embodiment Either stand position to shoot or clinostatism shooting, which has skew with vertical direction in the horizontal direction, is eliminating described first Image and second image after the matching double points of mistake that may be present, execute S106 in one direction, remove therebetween Along the matching double points of another direction mistake that may be present, namely with the difference of the second coordinate of first matching double points as horizontal stroke Coordinate, the first matching double points number corresponding with the difference of the second coordinate be vertical coordinate generate the second rectangular histogram, similarly, for The initial matching point centering eliminates the coupling of partial error for the first matching double points of rear acquisition, at described first Remain with the point correct matching double points of centering account for most, therefore, the first matching double points in second rectangular histogram When number sum is maximum, the first matching double points that its place cluster is included are correct matching double points (the final matching double points for determining) Probability be also maximum, therefore, retain in second rectangular histogram, the first matching double points number sum for maximum when its institute The first included matching double points in cluster, included the first matching double points in other clusters are gone as the matching double points of mistake Remove, and using the first matching double points for retaining as matching double points.In the present embodiment, if vertical position shoots then second coordinate being Abscissa, that is, generate with the difference of abscissa as abscissa, and the first matching double points number corresponding with the difference of abscissa is vertical coordinate The second rectangular histogram.If it is vertical coordinate that clinostatism shoots then second coordinate, that is, generate with the difference of vertical coordinate as abscissa, with Second rectangular histogram of the corresponding first matching double points number of the difference of vertical coordinate for vertical coordinate.Finally in second rectangular histogram, When determining that the first matching double points number sum is maximum, the first matching double points of place cluster are matching double points, and the width of the cluster can be with Between 4～6 pixels.By executing S106, the difference for eliminating abscissa is not belonging to most first for calculating With point to abscissa difference part, or the difference of vertical coordinate most first matching double points that are not belonging to calculate The part of the difference of vertical coordinate, namely eliminate wrong matching double points in first matching double points to obtain matching double points.

It should be noted that during the actual matching double points for removing the mistake in the initial matching point pair, can Can occur that the initial matching point in the first rectangular histogram included in different clusters, to number identical situation, is now needed to first Rectangular histogram carries out mean filter, retains initial matching point to number sum in the first rectangular histogram after to have passed through mean filter For maximum when its place cluster included in initial matching point to for the first matching double points, then with the of first matching double points The difference of two coordinates is abscissa, and the first matching double points number corresponding with the difference of the second coordinate is that vertical coordinate generates the second Nogata Figure, if there is also the first matching double points number identical situation included in different clusters in second rectangular histogram, now Mean filter need to be carried out to second rectangular histogram, determine the first matching double points in the second rectangular histogram that have passed through mean filter The first matching double points when number sum is maximum included in its place cluster are matching double points.

In the present embodiment, for the shooting of different positions, the angle of the position relationship between the first image and the second image Degree considered, first carries out statistics with histogram to the difference of the first coordinate of the initial matching point pair wherein wrong to remove Matching double points carry out statistics with histogram obtaining the first matching double points, then to the difference of the second coordinate of the first matching double points for obtaining With the further matching double points for removing the mistake that may possibly still be present, accurate matching double points are filtered out, is therefore improve most The accuracy of the matching double points for obtaining eventually, and match point other side is determined by the way of statistics with histogram in initial matching point pair Method simple computation amount is little.

Additionally, in the present embodiment, described first image and the second image are whole except existing with vertical direction in the horizontal direction Outside solid offsetting, it is also possible to there is the first image and the second image and there is larger skew in vertical direction and do not exist in the horizontal direction partially Move or skew is less, or the first image and the second image have larger skew in the horizontal direction and do not exist partially in vertical direction Move or offset less.For example：For above-mentioned X-ray shooting system, when detector and bulb vertically move (vertical position Shoot), or when moving (clinostatism shooting) in the horizontal direction, if the precision of place guide rail is sufficiently high when detector is moved, detection Device during vertically moving is moved in the horizontal direction less or there is no movement, and detector is along level Direction is less in vertical direction movement during moving or there is no movement, then between the first image and the second image The skew being likely to occur only in a direction is larger, and the skew in another direction is less or is substantially absent from, now Initial matching point, now can be from the more close vertical seat of statistics to being close to zero along the skew of a coordinate in other words in a direction The corresponding initial matching point of difference of mark to number, or from the corresponding initial matching point of the difference that counts more close abscissa to individual Count to remove wrong matching double points present in initial matching point pair.

Specifically, the difference in the present embodiment with the coordinate of the initial matching point pair is as abscissa, corresponding with the difference of coordinate Initial matching point be that vertical coordinate generates rectangular histogram to number, determine in the rectangular histogram that initial matching point is maximum to number sum When place cluster initial matching point to for matching double points.Similarly, for the initial matching detected under this application scenarios Point centering, account for most remain correct matching double points, therefore, included in a cluster in the rectangular histogram When initial matching point is most to number, the initial matching point included by its cluster being located is to the probability for correct matching double points Maximum, therefore initial matching point is to included initial matching point in its place cluster when number sum is maximum in reservation rectangular histogram Right, included initial matching point in other clusters is removed to the matching double points as mistake.In the present embodiment, for the first figure There is larger skew in vertical direction and there is no skew or skew less (vertical position shoots) in the horizontal direction in picture and the second image, The coordinate is vertical coordinate；Do not exist in vertical direction for the first image and the second image have larger skew in the horizontal direction Skew offsets less (clinostatism shooting), and the coordinate is abscissa；The width of the cluster before the rectangular histogram is generated, Need based on experience value or test the value for obtaining to be defined.In the present embodiment, the width of the cluster can be in 4～6 pixels Between point.

So far, achieved to the matching double points in the first image and the second image really by above-mentioned S101'～S106 Fixed, during determining matching double points, the detection complexity low velocity of characteristic point is fast, therefore also reduces to a certain extent Determine the complexity of matching double points, improve the speed for determining matching double points.Characteristic point due to detecting is more stable, therefore The accuracy of the initial matching point pair that coupling is obtained is also improved, in addition wrong coupling in the initial matching point pair is removed During point pair, from the point of view of the position relationship between the first image and the second image, and then by way of statistics with histogram Determine matching double points, method is simple, and amount of calculation is little, the accuracy of the matching double points of acquisition is high.Therefore, the present embodiment determines coupling Point to method, reduce to a certain extent determine matching double points complexity, improve to a certain extent determination coupling Point to speed, and the accuracy of the final matching double points for obtaining is high.

Corresponding to the method for above-mentioned determination matching double points, the present embodiment also provides a kind of device for determining matching double points, The device for determining matching double points includes：

Resolving cell, for carrying out pyramid decomposition to the first image and the second image, based on decomposition after the first image With the second image construct respectively the first difference pyramid diagram picture corresponding with described first image and with second image pair The the second difference pyramid diagram picture that answers；Described first image and second image are adjacent image；

Search unit, for traveling through each layer of the first difference pyramid diagram picture, searches for every layer of difference pyramid diagram Extreme point as in, travels through each layer of the second difference pyramid diagram picture, the pole that searches in every layer of difference pyramid diagram picture Value point；The extreme point is associated with the absolute value of the gray value of the pixel in default neighborhood；

First removal unit is strong for described first image in the extreme point of the first difference pyramid diagram picture for removing Pixel on edge is obtaining the characteristic point in described first image；Remove the extreme point of the second difference pyramid diagram picture In be pixel in the second image strong edge obtaining the characteristic point in second image；

Matching unit, initial to generate for being mated to the characteristic point in described first image and second image Matching double points；

First histogram production unit, for the difference of the first coordinate of the initial matching point pair as abscissa, with The corresponding initial matching point of the difference of one coordinate is that vertical coordinate generates the first rectangular histogram to number；

First matching double points determining unit, for determining that initial matching point is maximum to number sum in first rectangular histogram When place cluster initial matching point to for the first matching double points；

Second histogram production unit, for the difference of the second coordinate of first matching double points as abscissa, with The corresponding first matching double points number of the difference of two coordinates is that vertical coordinate generates the second rectangular histogram；

Matching double points determining unit, for determining the first matching double points number sum maximum when institute in second rectangular histogram It is matching double points in the first matching double points of cluster.

The device of the determination matching double points of the present embodiment, removes the matching double points of the initial matching point centering mistake to obtain The second removal unit for obtaining matching double points includes：First histogram production unit, the first matching double points determining unit, the second Nogata Figure signal generating unit and matching double points determining unit.Described determine matching double points device be embodied as refer to described determine With point to method enforcement, will not be described here.

The present embodiment also provides a kind of X-ray shooting system, including the device of above-mentioned determination matching double points.

Embodiment three

The present embodiment provides a kind of method for determining matching double points, from unlike embodiment two, removes in the present embodiment The method of the matching double points of the initial matching point centering mistake is different from embodiment two, refers to Fig. 4, and Fig. 4 is the present invention The schematic flow sheet of the method for the determination matching double points of embodiment three；In the present embodiment, the method bag for determining matching double points Include：

S105'：Slope of the initial matching point of described first image and the second image to place line is calculated, with described first The slope of beginning matching double points place line is abscissa, and initial matching point corresponding with the slope is vertical coordinate generation to number Slope histogram；

S106'：Determine initial matching of the initial matching point to number sum place cluster when maximum in the slope histogram Point is to for the first matching double points；

S107：Difference with the first coordinate of first matching double points as abscissa, corresponding with the difference of the first coordinate One matching double points number is that vertical coordinate generates the first rectangular histogram, determines the first matching double points number sum in first rectangular histogram When maximum, the first matching double points of place cluster are the second matching double points；

S108：Difference with the second coordinate of second matching double points as abscissa, corresponding with the difference of the second coordinate Two matching double points numbers are that vertical coordinate generates the second rectangular histogram, determine the second matching double points number sum in second rectangular histogram When maximum, the second matching double points of place cluster are matching double points.

In the present embodiment, S101 '～S104 generates initial matching point pair and the reality of described first image and second image Apply similar in example two, here is omitted, in the present embodiment, removing the initial of described first image and second image In matching double points during the matching double points of mistake, it is contemplated that the spy for the first image and the second image, with identical characteristics Line between levying a little should be parallel, such as：For the first image and the second image that are collected by X-ray shooting system, the two Line between the characteristic point in same anatomical should be parallel, correct between the first image and the second image in other words Line has between matching double points parallel nature (namely the slope of correct matching double points place line is identical or very It is close to), the line between characteristic point in non-equal anatomical structure can assume rambling state (the first image and the second figure Line as between the matching double points of mistake is in disorderly and unsystematic state, namely the slope of the matching double points place line of mistake It is in different value), therefore, in the present embodiment, statistics with histogram is carried out to the slope between initial matching point pair first, to pass through slope Rectangular histogram is removed wherein wrong matching double points come the initial matching point to generating to screening, then again from the first image And second the position relationship angle between image account for, carry out the initial matching point centering to the first image and the second image wrong Matching double points are further removed by mistake, are collected with the first image and the second image as X-ray shooting system below As a example by image, illustrate to removing the process of matching double points of initial matching point centering mistake, but technical scheme This is not limited.

Execute S105'：Calculate described first image I₁With the second image I₂Middle initial matching point is to the oblique of place line Rate, referring to Fig. 5, Fig. 5 be the embodiment of the present invention three the first image of acquisition and the second image in initial matching point to place line Slope schematic diagram, as shown in Figure 5：First image I described in the present embodiment₁With the second image I₂Close to and along level Direction is placed side by side, I₁The upper left corner be zero, I₁Horizontal boundary (length of horizontal boundary be I₁Wide W₁) and X-axis Overlap, (length on vertical border is the I on vertical border₁And I₂Height vertically) overlap with Y-axis, I₂Horizontal sides Boundary is also overlapped with X-axis.Pixel P₁And P₂For initial matching point pair, wherein pixel P₁It is located at I₁In, its coordinate is (x₁₁,y₁₁), Pixel P₂It is located at I₂In, due to I₁And I₂Place side by side, therefore, in coordinate system as shown in Figure 5, pixel P₂Coordinate be (x₂₁+W₁,y₂₂), m₁For pixel P₁With pixel P₂Between line, then m₁SlopeCan be obtained by equation below ?：

For I₁And I₂In all of initial matching point for, the slope of line can then pass through as follows therebetween Formula is obtained：

Wherein：K_iFor slope, (x of the i-th pair initial matching point to place line_1i,y_1i)、(x_2i,y_2i) it is that i-th pair is initial Matching double points, (x_1i,y_1i) it is described first image I₁In the position of i-th initial matching point, (x_2i,y_2i) it is described second Image I₂In the position of i-th initial matching point, W₁For described first image I₁Width.

Described first image I can be obtained by above-mentioned formula₁With the second image I₂In initial matching point to be located The slope of line, to calculate the slope of acquisition as abscissa, the number of initial matching point pair corresponding with the slope is vertical coordinate Generate slope histogram.For example, initial matching point is to P₁And P₂Between the slope of line beTo withCorresponding The number of initial matching point pair is counted, with continued reference to Fig. 5, with m in Fig. 5₁Parallel m₂、m₃、m₄Slope withPhase With, therefore, in Fig. 5 withThe number of corresponding initial matching point pair is 4.According to the statistical information, you can set up slope And the mapping relations between initial matching point pair, obtain slope histogram.By in the slope histogram to institute under Different Slope Statistics of the corresponding initial matching point to number, can remove the matching double points of partial error, this is because mating by S104 The initial matching point centering of acquisition, correct matching double points still account for major part, therefore, in the slope histogram one When initial matching point included in individual cluster is most to number, the initial matching point included by its cluster being located is to for correct With point to probability also maximum.

Therefore, S106' is executed, determines that initial matching point is to place cluster during number sum maximum in the slope histogram Initial matching point is to for the first matching double points.Namely retain in slope histogram, initial matching point is to when number sum is maximum Included initial matching point pair in its place cluster, using included initial matching point in other clusters to the match point as mistake To removing.In the present embodiment, the cluster can based on experience value depending on can also be obtained by experiment, namely generate described tiltedly Before rate rectangular histogram, need based on experience value or test the value for obtaining and the width of cluster is defined.In the present embodiment, the cluster Width can be between 4～6 pixels.For Fig. 5, exactly remain in Fig. 5 with m₁、m₂、m₃、m₄Corresponding first Beginning matching double points, remove and m₅、m₆Corresponding initial matching point pair.

It should be noted that in the present embodiment, being to set up coordinate system in the way shown in fig. 5 to obtain described first image With slope of the initial matching point to place line in the second image, in other embodiments, described first image and the second image Vertically can also place up and down, now, initial of described first image and second image under the coordinate system The expression way of the slope of place line is also slightly different with above-mentioned with, but for described first image and described second For image, no matter setting up coordinate system in which way, in the slope histogram for ultimately generating, the initial matching point of reservation is to individual When number sum is maximum, the initial matching point of place cluster is to all should be identical.Therefore, the first image described in the present embodiment and described The initial matching point of two images should not be used as the restriction to technical solution of the present invention to the calculation of the slope of place line.

From embodiment two, described first image and second image may be present in horizontally and vertically direction partially Move, still by taking X-ray shooting system as an example, when detector and vertically moving bulb, to the first image and the second image in Characteristic point mated, the difference of the vertical coordinate of the initial matching point pair of generation can bigger than the difference of abscissa (vertical direction inclined Move the skew more than horizontal direction).And detector and bulb are when moving in the horizontal direction, to the first image and the second image in Characteristic point is mated, and the difference of the abscissa of the initial matching point pair of generation can be than (the skew of horizontal direction greatly of the difference of vertical coordinate The skew of vertical direction can be more than).

Therefore, the error matching points in initial matching point pair is eliminated by slope histogram are to obtaining the first matching double points Afterwards, can be by way of statistics with histogram respectively from corresponding first matching double points of the difference that counts more close vertical coordinate Carry out in terms of number, the corresponding first matching double points number two of the difference of more close abscissa, so that remove can in the first matching double points The matching double points of the mistake that can also exist.Execute S107:Difference with the first coordinate of first matching double points as abscissa, with The corresponding first matching double points number of the difference of the first coordinate is that vertical coordinate generates the first rectangular histogram, determines in first rectangular histogram When first matching double points number sum is maximum, the first matching double points of place cluster are the second matching double points；In the present embodiment, if the Less, the skew in vertical direction is larger, such as skew between one image and the second image in the horizontal direction：Vertical position shoots, then The difference of the difference of the vertical coordinate of the matching double points of the difference of the vertical coordinate of correct matching double points and mistake in first matching double points Can be larger, so, first can carry out statistics with histogram to the difference of the vertical coordinate of first matching double points to obtain the second coupling Point is right, is exactly specifically with the difference of the vertical coordinate of first matching double points as abscissa corresponding with the difference of vertical coordinate first Matching double points number is that vertical coordinate generates the first rectangular histogram, determines that the first matching double points number sum is most in first rectangular histogram When big, the first matching double points of place cluster are the second matching double points.If in the inclined of vertical direction between the first image and the second image Move less, skew in the horizontal direction is larger, such as：Clinostatism shoot, then in the first matching double points correct matching double points horizontal seat The difference of the difference of the abscissa of the matching double points of the difference of mark and mistake also can be larger, so, can first to first match point To the difference of abscissa carry out statistics with histogram to obtain the second matching double points, specific with first matching double points The difference of abscissa is abscissa, and the first matching double points number corresponding with the difference of abscissa is that vertical coordinate generates the first rectangular histogram, When determining that the first matching double points number sum is maximum in first rectangular histogram, the first matching double points of place cluster match somebody with somebody point for second Right.

So that vertical position shoots as an example, the first image and the second image are respectively I₁And I₂, to I₁And I₂Carry out based on slope After the mode of statistics with histogram eliminates matching double points first matching double points of acquisition of partial error, if in the first matching double points Belong to I₁Pixel coordinate be (x_1i,y_1i), belong to I₂Pixel coordinate be (x_2i,y_2i), (i ∈ [1, N], N are The number of one matching double points), with y_2i-y_1iFor abscissa, and to y_2i-y_1iThe number of corresponding first matching double points is united Meter, such as：When the difference of the vertical coordinate of the first matching double points is 4 pixel units, the number of corresponding the first matching double points For 120, according to the statistical information, the difference pair with the difference of the vertical coordinate of the first matching double points as abscissa, with the vertical coordinate is generated First rectangular histogram of the number of the first matching double points that answers for vertical coordinate, by existing to the first matching double points in first rectangular histogram The statistics of the first corresponding matching double points number under the difference of different vertical coordinates, can remove the first matching double points of mistake, this It is because still account for major part by correct matching double points in the first matching double points of determination in S106', therefore, described When the first matching double points number included in a cluster in first rectangular histogram is most, included first of cluster which is located Maximum to the probability for correct matching double points with point, therefore, retain in first rectangular histogram, the first matching double points number Included the first matching double points in its place cluster when sum is maximum, using included the first matching double points in other clusters as The match point of mistake is removed, and using the first matching double points for retaining as the second matching double points.

For shooting similarly, for clinostatism, to the first image I₁With the second image I₂Carry out based on slope histogram After the mode of statistics eliminates matching double points first matching double points of acquisition of partial error, correct matching double points still account for greatly Part, therefore, with the difference of the abscissa of first matching double points as abscissa, corresponding with the difference of abscissa first mates When the first matching double points number included in a cluster in the first rectangular histogram that point is generated for vertical coordinate to number is most, its The first matching double points that the cluster at place is included are that the probability of correct matching double points is maximum, therefore retain first rectangular histogram In, when the first matching double points number sum is maximum, included the first matching double points in its place cluster, will be wrapped in other clusters The first matching double points for including are removed as the match point of mistake, and using the first matching double points for retaining as the second matching double points.

The vertical seat of most first matching double points that the difference for eliminating vertical coordinate by execution S107 is not belonging to calculate The part of the difference of mark, or the difference of the abscissa difference of the abscissa of most first matching double points that is not belonging to calculate Part, namely eliminate wrong matching double points in first matching double points to obtain the second matching double points.In the present embodiment, As described first image and second image have skew with vertical direction in the horizontal direction, therefore first is being eliminated Image I₁With the second image I₂In one direction after the matching double points of mistake that may be present, S108 is executed, edge therebetween is removed The matching double points of another direction mistake that may be present, namely with the difference of the second coordinate of second matching double points as horizontal seat Mark, the second matching double points number corresponding with the difference of the second coordinate are that vertical coordinate generates the second rectangular histogram, determine that described second is straight When in square figure, the second matching double points number sum is maximum, the second matching double points of place cluster are matching double points.In the present embodiment, if Offset less between described first image and the second image in the horizontal direction, the skew in vertical direction is larger, such as：Clap vertical position Take the photograph, then second coordinate is abscissa, that is, generate with the difference of abscissa as abscissa, corresponding with the difference of abscissa second Match somebody with somebody the second rectangular histogram that puts to number is vertical coordinate.If between described first image and the second image vertical direction skew not Greatly, in the horizontal direction skew is larger, such as：Clinostatism shoots, then second coordinate is vertical coordinate, that is, generate the difference with vertical coordinate For abscissa, second rectangular histogram of the second matching double points number corresponding with the difference of vertical coordinate for vertical coordinate.Final described the In two rectangular histograms, when determining that the second matching double points number sum is maximum, the second matching double points of place cluster are matching double points, described The width of cluster can be between 4～6 pixels.By executing S108, eliminate that the difference of abscissa is not belonging to calculate is big The part of the difference of the abscissa of the second partial matching double points, or the difference of vertical coordinate be not belonging to calculate most The part of the difference of the vertical coordinate of two matching double points, namely eliminate wrong matching double points in second matching double points to obtain Matching double points.

It should be noted that during the actual matching double points for removing the mistake in the initial matching point pair, can Can occur that the initial matching point in slope histogram included in different clusters, to number identical situation, is now needed to slope Rectangular histogram carries out mean filter, retains initial matching point to number sum in the slope histogram after to have passed through mean filter For maximum when its place cluster included in initial matching point to for the first matching double points, then with the of first matching double points The difference of one coordinate is abscissa, and the first matching double points number corresponding with the difference of the first coordinate is that vertical coordinate generates the first Nogata Figure, if there is also the first matching double points number identical situation included in different clusters in first rectangular histogram, now Mean filter need to be carried out to first rectangular histogram, retain first in the first rectangular histogram after to have passed through mean filter and mate Point to the first matching double points included in its place cluster when number sum is maximum be the second matching double points, then with this second The difference of the second coordinate of matching double points is abscissa, and the second matching double points number corresponding with the difference of the second coordinate is that vertical coordinate is given birth to Into the second rectangular histogram, if still suffering from the second matching double points number identical feelings included in different clusters in second rectangular histogram Condition, then carry out mean filter to second rectangular histogram, determines the second match point in the second rectangular histogram that have passed through mean filter The second matching double points when being maximum to number sum included in its place cluster are matching double points.

In the present embodiment, for being offset between described first image and the second image in the horizontal direction less, in vertically side To skew larger, such as：Vertical position shoots, and first eliminates initial matching point centering portion mistake based on the mode of slope histogram Matching double points obtain the first matching double points, and then the difference with the vertical coordinate of first matching double points is as abscissa, with vertical coordinate The corresponding first matching double points number of difference be that vertical coordinate generates the first rectangular histogram to determine the second matching double points, finally with described The difference of the abscissa of the second matching double points is abscissa, and the second matching double points number corresponding with the difference of abscissa is that vertical coordinate is given birth to Into the second rectangular histogram determining matching double points.For between described first image and the second image vertical direction skew not Greatly, in the horizontal direction skew is larger, such as：Clinostatism shoots, and first eliminates initial matching point pair based on the mode of slope histogram The matching double points of middle partial error obtain the first matching double points, and then the difference with the abscissa of first matching double points is as horizontal seat Mark, the first matching double points number corresponding with the difference of abscissa are that vertical coordinate generates the first rectangular histogram to determine the second match point Right, finally the difference with the vertical coordinate of second matching double points is as abscissa, the second matching double points corresponding with the difference of vertical coordinate Number is that vertical coordinate generates the second rectangular histogram to determine matching double points.For the shooting of different positions, the first image had both been considered And the second anatomical structure characteristic between image, and the angle of the position relationship between the first image and the second image carries out Consider to remove the match point of the initial matching point centering mistake of the first image and the second image as far as possible, therefore in very great Cheng On improve final acquisition matching double points accuracy, and determined in initial matching point pair by the way of statistics with histogram Matching double points method simple computation amount is little.

The method of the determination matching double points of the present embodiment, when feature point detection is carried out, detects that the complexity of characteristic point is low, Speed is fast, therefore reduces the complexity for determining matching double points to a certain extent, improves the speed for determining matching double points.By More stable in the characteristic point for detecting, the accuracy of the initial matching point pair that coupling is obtained therefore is also improved, described removing During the matching double points of initial matching point centering mistake, by the way of based on slope histogram statistics and statistics with histogram, remove mistake Matching double points, complexity is low, amount of calculation is little, and the accuracy of the matching double points of acquisition is high by mistake.Therefore, the determination coupling of the present embodiment Point to method, reduce to a great extent determine matching double points complexity, improve to a great extent determination coupling Point to speed, and the accuracy of the final matching double points for obtaining is high.

From embodiment two, described first image and second image are except existing with vertical direction in the horizontal direction Outside overall offset, it is also possible to there is described first image and second image and there is larger skew in level side in vertical direction To there is no skew or offseting less, or there is larger skew in described first image and second image in the horizontal direction There is no skew in vertical direction or offset less.For example：For above-mentioned X-ray shooting system, when detector and bulb (vertical position shoot) is vertically moved, or when moving (clinostatism shooting) in the horizontal direction, if be located when detector is moved leading The precision of rail is sufficiently high, and detector during vertically moving is moved in the horizontal direction less or be there is no Mobile, detector is less in vertical direction movement during moving in the horizontal direction or there is no movement, initially Skew of the matching double points in a direction in other words along a coordinate is close to zero.

Therefore, in another embodiment, statistics with histogram can be carried out to the slope between initial matching point pair first, is passed through Slope histogram is removed wherein wrong matching double points come the initial matching point to generating to screening, then again from statistics The corresponding initial matching point of the difference of more close vertical coordinate is to number or corresponding just from the difference for counting more close abscissa Beginning matching double points number is removing the matching double points of the mistake that initial matching point centering is still suffered from.

Specifically, it is exactly first to calculate the initial matching point of described first image and second image to the oblique of place line Rate, with the initial matching point to the slope of place line as abscissa, initial matching point corresponding with the slope is to number Slope histogram is generated for vertical coordinate；Determine that initial matching point is to place cluster during number sum maximum in the slope histogram Initial matching point is to for the first matching double points；Difference pair with the difference of the coordinate of first matching double points as abscissa, with coordinate The the first matching double points number that answers is that vertical coordinate generates rectangular histogram；Determine that the first matching double points number sum is most in the rectangular histogram When big, the first matching double points of place cluster are matching double points.If described first image and second image are present in vertical direction There is no skew in larger skew or skew less in the horizontal direction, and such as vertical position shoots, then with the vertical of first matching double points The difference of coordinate is abscissa, and the first matching double points number corresponding with the difference of vertical coordinate is that vertical coordinate generates rectangular histogram.If described First image and second image exist in the horizontal direction larger skew vertical direction do not exist skew or offset less, As clinostatism shoots, then the difference with the abscissa of first matching double points is as abscissa, corresponding with the difference of abscissa first It is that vertical coordinate generates rectangular histogram to number with point.

In another embodiment, it is also possible to by way of slope histogram, only remove the initial matching point centering mistake Matching double points.

So far, the initial matching point centering of described first image and second image is eliminated by S101 '～S108 The matching double points of the mistake of presence, in the present embodiment, first eliminate initial matching point to middle part based on the mode of slope histogram Misclassification matching double points by mistake obtain the first matching double points, and then the difference with the first coordinate of first matching double points is as horizontal seat Mark, the first matching double points number corresponding with the difference of the first coordinate are that vertical coordinate generates the first rectangular histogram to determine the second match point Right, finally with the second of second matching double points the difference that marks as abscissa, the second match point corresponding with the difference of the second coordinate It is that vertical coordinate generates the second rectangular histogram to determine matching double points to number.Both solution first image and second image between had been considered Architectural characteristic is cutd open, and the angle of the position relationship between the first image and the second image has carried out considering to remove as far as possible The match point of the initial matching point centering mistake of the first image and the second image, therefore improves final acquisition on very great Cheng The accuracy of matching double points, reduces the complexity for obtaining matching double points to a great extent, and using slope histogram and straight The mode of side's figure statistics determines that in initial matching point pair matching double points method simple computation amount is little.

The method of corresponding above-mentioned determination matching double points, the present embodiment also provide a kind of device for determining matching double points, institute State and determine that the device of matching double points includes：

Resolving cell：For carrying out pyramid decomposition to the first image and the second image, based on decomposition after the first image With the second image construct respectively the first difference pyramid diagram picture corresponding with described first image and with second image pair The the second difference pyramid diagram picture that answers；Described first image and second image are adjacent image；

Search unit：For traveling through each layer of the first difference pyramid diagram picture, every layer of difference pyramid diagram is searched for Extreme point as in, travels through each layer of the second difference pyramid diagram picture, the pole that searches in every layer of difference pyramid diagram picture Value point, the extreme point are associated with the absolute value of the gray value of the pixel in default neighborhood；

First removal unit：Strong for described first image in the extreme point of the first difference pyramid diagram picture for removing Pixel on edge is obtaining the characteristic point in described first image；Remove the extreme point of the second difference pyramid diagram picture In be pixel in the second image strong edge obtaining the characteristic point in second image；

Matching unit：Initial to generate for being mated to the characteristic point in described first image and second image Matching double points；

Slope histogram signal generating unit：Connect to being located for calculating the initial matching point of described first image and the second image The slope of line, with the initial matching point to the slope of place line as abscissa, initial matching point corresponding with the slope It is that vertical coordinate generates slope histogram to number；

First matching double points determining unit：For determining that initial matching point is maximum to number sum in the slope histogram When place cluster initial matching point to for the first matching double points；

First histogram production unit：For with the difference of the first coordinate of first matching double points as abscissa, with The corresponding first matching double points number of the difference of one coordinate is that vertical coordinate generates the first rectangular histogram；

Second matching double points determining unit：For determining that the first matching double points number sum is maximum in first rectangular histogram When place cluster the first matching double points be the second matching double points；

Second histogram production unit：For with the difference of the second coordinate of second matching double points as abscissa, with The corresponding second matching double points number of the difference of two coordinates is that vertical coordinate generates the second rectangular histogram；

Matching double points determining unit, for determining the second matching double points number sum maximum when institute in second rectangular histogram It is matching double points in the second matching double points of cluster.

The device of the determination matching double points of the present embodiment, removes the matching double points of the initial matching point centering mistake to obtain The second removal unit for obtaining matching double points includes：Slope histogram signal generating unit, the first matching double points determining unit, the first Nogata Figure signal generating unit, the second matching double points determining unit, the second histogram production unit and matching double points determining unit.The determination The enforcement for being embodied as referring to the method for determining matching double points of the device of matching double points, will not be described here.

Example IV

The present embodiment provides a kind of image acquiring method, refers to Fig. 6, and Fig. 6 is that the image of the embodiment of the present invention four is obtained The schematic flow sheet of method, as shown in Figure 6：Described image acquisition methods include：

S201：Pyramid decomposition is carried out to the first image and the second image, based on decomposition after the first image and the second figure As construct respectively corresponding with described first image the first difference pyramid diagram picture and with second image corresponding second Difference pyramid diagram picture；Described first image and second image are adjacent image；

S202：Each layer of the first difference pyramid diagram picture is traveled through, the pole that searches in every layer of difference pyramid diagram picture Value point, travels through each layer of the second difference pyramid diagram picture, and the extreme point that searches in every layer of difference pyramid diagram picture is described Extreme point is associated with the absolute value of the gray value of the pixel in default neighborhood；

S203：Remove in the extreme point of the first difference pyramid diagram picture as the pixel in described first image strong edge Put to obtain the characteristic point in described first image；It is described second to remove in the extreme point of the second difference pyramid diagram picture Pixel in image strong edge is obtaining the characteristic point in second image；

S204：Characteristic point in described first image and second image is mated to generate initial matching point Right；

S205：Remove the matching double points of the initial matching point centering mistake to obtain matching double points；

S206：Determined between described first image and second image based on the position relationship between the matching double points Skew；

S207：Described first image and described are determined according to the skew between described first image and second image The overlapping region of two images；

S208：Described first image and second image are spliced according to the overlapping region.

The image obtained in the present embodiment is spliced image, and described first image and second image can be logical Cross the image that X-ray shooting system is collected, due to described first image and second image are carried out feature point detection and Coupling, is mainly used for splicing the first image and the second image, therefore, in the present embodiment before S201 is executed, can First to carry out pretreatment to described first image and second image, specifically, can be estimated according to actual experience first Described first image and the initial overlapping region of second image, according to the initial of described first image and second image The height of overlapping region determines first area corresponding with the initial overlapping region in described first image, claims in the present embodiment Be the 3rd image, determine second area corresponding with the initial overlapping region in second image, title in the present embodiment Be the 4th image.Pretreatment is carried out to the 3rd image and the 4th image, is exactly specifically to the 3rd image Tonal range mapping is carried out with the 4th image, the gray value of the 3rd image and the 4th image is mapped to identical Scope.For example, if the gray value of the 3rd image belongs to 0～255, the gray value of the 4th image belongs to 0～ 4096, then the 0～4096 of the 4th image can be mapped to by linear for the 0～255 of the 3rd image gray value In intensity value ranges.

Furthermore, it is contemplated that when splicing to described first image and second image, region (real weight to be spliced Folded region) belong to the initial overlapping region, so in detection described first image and second image in the present embodiment In characteristic point when, that is, execute S201～S203 during, be the part area to described first image and second image Domain is that above-mentioned initial overlapping region is detected, namely the of the first area to described first image and second image One region is detected, or perhaps the characteristic point to the 3rd image and the 4th image is detected.

Therefore, when executing S201 during stitching image is obtained, it is the parts of images to described first image and institute The parts of images for stating the second image carries out pyramid decomposition, that is to say, that be to the 3rd image and the 4th figure in S201 As carrying out pyramid decomposition, and based on decomposition after the 3rd image and the 4th image construct respectively corresponding with the 3rd image 3rd difference pyramid diagram picture and the 4th difference pyramid diagram picture corresponding with the 4th image.And it is concrete how to the 3rd Image and the 4th image carry out pyramid decomposition, and construct the 3rd difference pyramid diagram picture and the 4th difference pyramid diagram picture, can So that referring to S101, here is omitted.

Similarly, when executing S202 during stitching image is obtained, it is traversal the 3rd difference pyramid diagram picture Each layer, the extreme point that searches in every layer of difference pyramid diagram picture travels through each layer of the 4th difference pyramid diagram picture, Extreme point in every layer of difference pyramid diagram picture of search, the extreme point are associated with the gray value of the pixel in default neighborhood Absolute value；When executing S203, it in the extreme point for remove the 3rd difference pyramid diagram picture is the 3rd image strong edge to be On pixel obtaining the characteristic point in the 3rd image；In the extreme point for removing the 4th difference pyramid diagram picture it is Pixel in the 4th image strong edge is to obtain the characteristic point in the 4th image, and the spy in the 3rd image The characteristic point a little and in described first image is levied, the characteristic point in the 4th image is also the feature in second image Point.

Additionally, from the foregoing, determine difference of Gaussian pyramid diagram picture in extreme point when, the extreme point can be The pixel P of top N is located at after sorting according to order from large to small to the absolute value of the gray value of pixel P, is spelled obtaining During map interlinking picture, when determining the extreme point in the 3rd difference pyramid diagram picture, the extreme point for being taken can be then right According to the pixel P for being located at front M positions after order sequence from large to small, (pixel P is referred to the absolute value of the gray value of pixel P In per layer of the 3rd difference pyramid diagram picture, the absolute value of the gray value of pixel is picture in the default neighborhood of the pixel The pixel of the maximum absolute value of the gray value of vegetarian refreshments), M=N × initial overlapping region height.Similarly, determine the described 4th During extreme point in difference pyramid diagram picture, the extreme point for being taken can also be the gray value to pixel P absolute value according to After order sequence from large to small, positioned at the pixel P of front M positions, (pixel P refers to the every of the 4th difference pyramid diagram picture In layer, the absolute value of the gray value of pixel is the maximum absolute value of the gray value of pixel in the default neighborhood of the pixel Pixel).

The present embodiment when stitching image is obtained, due to considering the detection to characteristic point and coupling is mainly used for the One image and the second image are spliced, and therefore, when characteristic point is detected, the first image of view picture and the second image are not carried out Detection, but it is pointed to the initial overlapping region in the first image i.e. the 3rd image and the initial overlay region in the second image Domain is detection and the coupling that the 4th image has carried out characteristic point, therefore can improve the detection speed of characteristic point, further The speed of the matching double points for determining described first image and second image can be improved.

After the characteristic point in execution S201～S203 acquisition described first images and second image, S204 is executed ～S205 is mated to generate initial matching point pair to the characteristic point in described first image and second image, removes institute State the matching double points of initial matching point centering mistake to obtain matching double points.To described first image and described in the present embodiment How characteristic point in two images carries out mating the description that may refer in embodiment two, removes the initial matching point centering wrong Matching double points by mistake are described initial to obtain the removal that matching double points are then may refer to described in embodiment two and embodiment three The method of the matching double points with a centering mistake, here is omitted.

After matching double points being obtained by S201～S205, execute S206 based on the position relationship between the matching double points Determine the skew between described first image and second image.Specifically, it is with the horizontal seat of matching double points in the present embodiment The average of the difference of mark is used as the skew between described first image and second image in the horizontal direction, indulging with matching double points The average of the difference of coordinate is used as the skew between described first image and second image in vertical direction.For example：If The final matching double points for obtaining are K, wherein belong to the first image I₁Pixel coordinate be (x_1i,y_1i), belong to the second figure As I₂Pixel coordinate be (x_2i,y_2i), then the skew between described first image and second image in the horizontal directionBetween described first image and second image vertical direction skew

In the present embodiment, if the skew between described first image and second image in the horizontal direction is very little (such as： Vertical position shoots or longitudinal spliced), therefore Δ x goes to zero, if in vertical direction between described first image and second image Skew very little (such as：Clinostatism shoots or horizontally-spliced), then Δ y goes to zero.

S207 is executed, between the described first image obtained according to S206 and second image in the horizontal direction and vertically The skew in direction determines overlapping region therebetween.From the foregoing, being to estimate institute according to actual experience in the present embodiment The initial overlapping region of the first image and second image is stated, to described first image and described only in initial overlapping region The characteristic point of the second image is detected, can so improve the detection speed of characteristic point, and then improve determination matching double points Speed and the speed of image mosaic.Therefore in this step, still with described first image I₁With the second image I₂In, respectively It is extracted initial overlapping region I₁' (the 3rd image) and initial overlapping region I₂' (the 4th image) (I₁' and I₂' picture size Identical) to initial overlapping region I₁' and I₂' carry out feature point detection and coupling after obtain I₁' and I₂' between in the horizontal direction Shifted by delta x, as a example by shifted by delta y of vertical direction, illustrate how obtain I₁' and I₂' between overlapping region.

Specifically, I is determined as follows in the present embodiment₁' and I₂' between overlapping region.Set up in the present embodiment Level is X-axis positive direction to the right, is the rectangular coordinate system of Y-axis positive direction straight down, due to I₁' and I₂' picture size phase With, therefore I₁' and I₂' height H (along Y direction) and I₁' and I₂' width W (along X-direction) all same.With I₂' top Boundary and I₁' coboundary between relative position relation as I₁' and I₂' vertical direction shifted by delta y, with I₂' left margin With I₁' left margin relative position relation as I₁' and I₂' shifted by delta x in the horizontal direction.May in actual splicing Occur that Δ x, Δ y are all higher than zero, Δ x is less than zero more than zero, Δ y, and Δ x is more than zero less than zero, Δ y, and Δ x, Δ y are respectively less than zero Situation, below in conjunction with the position relationship schematic diagram between first second image of image of Fig. 7-a to Fig. 7-d, to these four feelings Under condition, the determination of overlapping region carries out simple illustration.

Furthermore, it is necessary to illustrate, Fig. 7-a to Fig. 7-d are only that the position relationship between the first image and the second image shows It is intended to, it illustrates the situation that there is skew between the first image and the second image in the horizontal direction with vertical direction, not Illustrate between the first image that may be present and the second image in the horizontal direction or vertical direction the feelings that go to zero of skew Condition, but technical scheme is not limited to the situation shown in Fig. 7-a to Fig. 7-d.

In the present embodiment, if Δ x, Δ y are all higher than zero, as shown in Fig. 7-a, I₂' coboundary and I₁' coboundary equal It is located at Y-axis positive, I₂' coboundary be located at I₁' coboundary lower section, I₂' coboundary relative to I₁' coboundary along Y-axis Positive skew is I₁' and I₂' vertical direction shifted by delta y, now I₁' and I₂' vertical direction overlap height be H- Δs y；I₂' left margin and I₁' left margin positive, the I that is respectively positioned on X-axis₂' left margin be located at I₁' left margin right, I₂' Left margin is relative to I₁' left margin be I along the positive skew of X-axis₁' and I₂' shifted by delta x in the horizontal direction, I₁' and I₂' The width that horizontal direction is overlapped is W- Δ x.Determining I₁' and I₂' in the horizontal direction with vertical direction overlap width after, then Can determine I₁' and I₂' overlapping region, and I₁' and I₂' between overlapping region, namely I₁And I₂Between overlapping region.

If Δ x is less than zero, as shown in Fig. 7-b, I more than zero, Δ y₂' coboundary be located at Y-axis negative sense, I₁' coboundary It is located at X-axis, I₂' coboundary be located at I₁' coboundary top, I₂' coboundary relative to I₁' coboundary skew along Y Axle negative sense, now I₁' and I₂' vertical direction overlap height be H+ Δ y；I₂' left margin and I₁' left margin be respectively positioned on X Axle is positive, I₂' left margin be located at I₁' left margin right, I₂' left margin relative to I₁' left margin positive along X-axis Skew be I₁' and I₂' shifted by delta x in the horizontal direction, I₁' and I₂' the width that overlaps in the horizontal direction is W- Δ x.Determining I₁' and I₂' in the horizontal direction with vertical direction overlap width after, then can determine I₁' and I₂' overlapping region, and I₁' and I₂' between overlapping region, namely I₁And I₂Between overlapping region.

If Δ x is more than zero, as shown in Fig. 7-c, I less than zero, Δ y₂' coboundary and I₁' coboundary be respectively positioned on Y-axis Forward direction, I₂' coboundary be located at I₁' coboundary lower section, now I₁' and I₂' vertical direction overlap height be H- Δ y； I₂' left margin be located at Y-axis, I₁' left margin positive, the I that is located at X-axis₂' left margin be located at I₁' left margin left, I₂′ Left margin relative to I₁' left margin skew along X-axis negative sense, I₁' and I₂' the width that overlaps in the horizontal direction is W+ Δ x. Determining I₁' and I₂' in the horizontal direction with vertical direction overlap width after, then can determine I₁' and I₂' overlapping region, And I₁' and I₂' between overlapping region, namely I₁And I₂Between overlapping region.

If Δ x is less than zero, as shown in Fig. 7-d, I less than zero, Δ y₂' coboundary be located at Y-axis negative sense, I₁' coboundary It is located at X-axis, I₂' coboundary be located at I₁' coboundary top, I₂' coboundary relative to I₁' coboundary skew along Y Axle negative sense, now I₁' and I₂' vertical direction overlap height be H+ Δ y；I₂' left margin be located at Y-axis, I₁' left margin It is located at X-axis positive, I₂' left margin be located at I₁' left margin left, I₂' left margin relative to I₁' left margin inclined Move along X-axis negative sense, I₁' and I₂' the width that overlaps in the horizontal direction is W+ Δ x.Determining I₁' and I₂' in the horizontal direction and perpendicular Nogata then can determine I to after the width for overlapping₁' and I₂' overlapping region, and I₁' and I₂' between overlapping region, namely I₁ And I₂Between overlapping region.

It should be noted that being with I in the present embodiment₂' coboundary and I₁' coboundary between relative position relation make For I₁' and I₂' vertical direction shifted by delta y, with I₂' left margin and I₁' left margin relative position relation as I₁' and I₂' shifted by delta x in the horizontal direction.In other embodiments, I₁' and I₂' can also be with I in shifted by delta y of vertical direction₂' Lower boundary and I₁' lower boundary between relative position relation depending on, I₁' and I₂' shifted by delta x in the horizontal direction can also be with I₂' right margin and I₁' right margin relative position relation depending on, this is not limited in the present embodiment.

S208 is executed, according to the overlapping region of first image and the second image of S207 acquisitions, to spelling therebetween Connect, still with above-mentioned I₁And I₂As a example by, from the foregoing, being actually to the first image I in S207₁With the second image I₂In Initial overlapping region I₁' and I₂' overlapping region determined, and I₁' and I₂' overlapping region namely I₁And I₂Overlay region Domain.In the present embodiment, using the center of the overlapping region as splice point, first determine splice point in I₁' and I₂' in position coordinateses, Due to I₁' and I₂' and original image I₁And I₂Between relative position relation be known, therefore could be aware that splice point in I₁Position Put and splice point is in I₂Position, the position for being then based on splice point is come to I₁And I₂Spliced, and to spliced image Merged.Specifically, still set up in the present embodiment with level to the right as X-axis forward direction, be that the positive right angle of Y-axis is sat straight down Mark system, I₁And I₂The upper left corner be respectively positioned on the origin of the coordinate system, with I₁For the piece image for collecting, I₂Collect As a example by second sub-picture, then in I₁The middle vertical coordinate for retaining pixel is less than I₁In splice point vertical coordinate pixel be located Region, in I₂Region of the middle vertical coordinate for retaining pixel more than the pixel place of the vertical coordinate of splice point, by this two Spliced so that being located at I subregion₁And I₂In splice point overlap.Due to I₁And I₂Between there is gray difference, therefore exist To I₁And I₂After being spliced, can there is obvious gray difference in the image above and below stitching portion, thus need to be to splicing after Image merged.Specifically, splice point is crossed in spliced image and do the straight line parallel with X-axis, be called I₁And I₂It Between piece, in the top of the piece (for I₁) and lower section (for I₂) N number of pixel is respectively taken, each picture in 2N pixel The gray value g of vegetarian refreshments is obtained by equation below：

G=a₁×I₁(x₁,y₁)+a₂×I₂(x₂,y₂)

Wherein, a₁+a₂=1, I₁(x₁,y₁) for the pixel in I₁In gray value, I₂(x₂,y₂) for the pixel in I₂ In gray value.a₁And a₂For weight, when every string pixel position from being located at I₁To piece is moved closer to, it is then within Piece, then from away from piece to be located at I₂In, weight a in above-mentioned formula₁0 is gradually become by 1, weight a₂1 is gradually become by zero, Cause the transition in the region near piece smoother by average weighted mode so that spliced image is more accorded with Close actual clinical demand.

In practical application, to I₁And I₂After completing splicing, (part does not have to produce therebetween the relative part for offseting Image) can be filled using black, or corresponding using carrying out to the gray value of the background area that the Offset portion is close to Filling.

So far, achieved by above-mentioned step and the characteristic point in described first image and second image is examined Survey, and which is mated, generate matching double points, described first image and second figure is finally determined according to matching double points The overlapping region of picture, and then complete the splicing to described first image and second image.Complexity is employed in the present embodiment Degree is low, and amount of calculation is little and fireballing mode determines the high matching double points of accuracy, and then is spelled based on the matching double points The splicing high precision of the image obtained after connecing.

Corresponding above-mentioned image acquiring method, the present embodiment also provide a kind of image acquiring device, and described image obtains dress Put including：

Second removal unit, for removing the matching double points of the initial matching point centering mistake to obtain matching double points；

Offset-determining unit, for determining described first image and described based on the position relationship between the matching double points Skew between second image；

Overlapping region determining unit, described in determining according to the skew between described first image and second image First image and the overlapping region of second image；

Concatenation unit, for splicing to described first image and second image according to the overlapping region.

The enforcement for being embodied as referring to described image acquisition methods of described image acquisition device, will not be described here.

The present embodiment also provides a kind of X-ray shooting system, including above-mentioned image acquiring device.

In sum, the detection method of characteristic point in the image that embodiment of the present invention is provided, at least has beneficial as follows Effect：

Although the present invention is disclosed as above with preferred embodiment, which is not any this area for limiting the present invention Technical staff without departing from the spirit and scope of the present invention, may be by the methods and techniques content of the disclosure above to this Bright technical scheme makes possible variation and modification, and therefore, every content without departing from technical solution of the present invention, according to the present invention Technical spirit any simple modification, equivalent variations and modification that above example is made, belong to technical solution of the present invention Protection domain.

Claims

1. a kind of determine matching double points method, it is characterised in that include：

Pyramid decomposition is carried out to the first image and the second image, based on decomposition after the first image and the second image construct respectively The first difference pyramid diagram picture corresponding with described first image and the second difference pyramid corresponding with second image Image；Described first image and second image are adjacent image；

Each layer of the first difference pyramid diagram picture is traveled through, the extreme point that searches in every layer of difference pyramid diagram picture, traversal Each layer of the second difference pyramid diagram picture, the extreme point that searches in every layer of difference pyramid diagram picture；The extreme point is closed The absolute value of the gray value of the pixel being coupled in default neighborhood；

Remove is the pixel in described first image strong edge in the extreme point of the first difference pyramid diagram picture obtaining Characteristic point in described first image；It is the strong side of second image in the extreme point for removing the second difference pyramid diagram picture Pixel on edge is obtaining the characteristic point in second image；

The characteristic vector of the characteristic point in the characteristic vector of the characteristic point in based on described first image and second image is right Characteristic point in described first image and second image carries out bi-directional matching to generate initial matching point pair；

2. as claimed in claim 1 determine matching double points method, it is characterised in that the first difference pyramid diagram as Extreme point refers to that the absolute value of the gray value of pixel in every layer of difference pyramid diagram picture is picture in the default neighborhood of the pixel The pixel of the maximum absolute value of the gray value of vegetarian refreshments；The extreme point of the second difference pyramid diagram picture refers to every layer of difference gold In word tower image, the absolute value of the gray value of pixel is the absolute value of the gray value of pixel in the default neighborhood of the pixel Maximum pixel.

3. as claimed in claim 1 determine matching double points method, it is characterised in that the first difference pyramid diagram as Extreme point refers to that revised pixel P in the first difference pyramid diagram picture, revised pixel P are to pixel P Position be modified after obtain, the pixel P refers to pixel in every tomographic image of the first difference pyramid diagram picture The absolute value of the gray value of point is the pixel of the maximum absolute value of the gray value of pixel in the default neighborhood of the pixel；Institute The extreme point for stating the second difference pyramid diagram picture refers to revised pixel P in the second difference pyramid diagram picture, described Revised pixel P is obtained after the position to pixel P is modified, and pixel P refers to the second difference gold word In every tomographic image of tower image, the absolute value of the gray value of pixel is the gray value of pixel in the default neighborhood of the pixel Maximum absolute value pixel.

4. as claimed in claim 1 determine matching double points method, it is characterised in that the first difference pyramid diagram as Extreme point refers to the absolute value of the gray value to pixel P according to the pixel for being located at top N after order sequence from large to small P, the pixel P refer to that the absolute value of the gray value of pixel in every tomographic image of the first difference pyramid diagram picture is institute State the pixel of the maximum absolute value of the gray value of pixel in the default neighborhood of pixel；The second difference pyramid diagram as Extreme point refers to the absolute value of the gray value to pixel P according to the pixel for being located at top N after order sequence from large to small P, the pixel P refer to that the absolute value of the gray value of pixel in every tomographic image of the second difference pyramid diagram picture is institute State the pixel of the maximum absolute value of the gray value of pixel in the default neighborhood of pixel.

5. as claimed in claim 1 determine matching double points method, it is characterised in that described based on described first image in The characteristic vector of the characteristic point in the characteristic vector of characteristic point and second image, to described first image and second figure Characteristic point as in carries out bi-directional matching to generate initial matching point to including：

With the feature of the characteristic point in the characteristic vector of each characteristic point in described first image and second image to Minimum euclidean distance between amount is the first ratio with the ratio of secondary little Euclidean distance；

When first ratio is less than first threshold, with the spy in described first image corresponding with the minimum euclidean distance It is the 3rd matching double points to levy the characteristic point a little and in second image, generates the first collection with the 3rd matching double points as element Close；

With the feature of the characteristic point in the characteristic vector of each characteristic point in second image and described first image to Minimum euclidean distance between amount is the second ratio with the ratio of secondary little Euclidean distance；

When second ratio is less than Second Threshold, with the spy in second image corresponding with the minimum euclidean distance It is the 4th matching double points to levy the characteristic point a little and in described first image, generates the second collection with the 4th matching double points as element Close；

Take the common factor of the first set and the second set to obtain the initial of described first image and second image Matching double points.

6. as claimed in claim 5 determine matching double points method, it is characterised in that

The characteristic vector of the characteristic point in the characteristic vector of each characteristic point in described first image and second image Between minimum euclidean distance refer to the ratio of secondary little Euclidean distance：The feature of each characteristic point in described first image to Amount with second image with the abscissa of this feature point apart from its poor absolute value less than the first predeterminable range and with this The minimum euclidean distance that characteristic point belongs between the characteristic vector of the characteristic point of same difference pyramid image layer is little European with secondary The ratio of distance；

The characteristic vector of the characteristic point in the characteristic vector of each characteristic point in second image and described first image Between minimum euclidean distance refer to the ratio of secondary little Euclidean distance：The feature of each characteristic point in second image to Amount with described first image with the abscissa of this feature point apart from its poor absolute value less than the second predeterminable range and with this The minimum euclidean distance that characteristic point belongs between the characteristic vector of the characteristic point of same difference pyramid image layer is little European with secondary The ratio of distance.

7. as claimed in claim 5 determine matching double points method, it is characterised in that

The characteristic vector of the characteristic point in the characteristic vector of each characteristic point in described first image and second image Between minimum euclidean distance refer to the ratio of secondary little Euclidean distance：The feature of each characteristic point in described first image to Amount with second image with the vertical coordinate of this feature point apart from its poor absolute value less than the 3rd predeterminable range and with this The minimum euclidean distance that characteristic point belongs between the characteristic vector of the characteristic point of same difference pyramid image layer is little European with secondary The ratio of distance；

The characteristic vector of the characteristic point in the characteristic vector of each characteristic point in second image and described first image Between minimum euclidean distance refer to the ratio of secondary little Euclidean distance：The feature of each characteristic point in second image to Amount with described first image with the vertical coordinate of this feature point apart from its poor absolute value less than the 4th predeterminable range and with this The minimum euclidean distance that characteristic point belongs between the characteristic vector of the characteristic point of same difference pyramid image layer is little European with secondary The ratio of distance.

8. the method for determining matching double points as claimed in claim 1, it is characterised in that the removal initial matching point pair The matching double points of middle mistake are included with obtaining matching double points：

Slope of the initial matching point of described first image and second image to place line is calculated, with the initial matching Point is abscissa to the slope of place line, and initial matching point corresponding with the slope is that vertical coordinate generation slope is straight to number Fang Tu；

Difference with the first coordinate of first matching double points as abscissa, the first matching double points corresponding with the difference of the first coordinate Number is that vertical coordinate generates the first rectangular histogram；

Difference with the second coordinate of second matching double points as abscissa, the second matching double points corresponding with the difference of the second coordinate Number is that vertical coordinate generates the second rectangular histogram；

When determining that the second matching double points number sum is maximum in second rectangular histogram, the second matching double points of place cluster are coupling Point is right.

9. as claimed in claim 8 determine matching double points method, it is characterised in that calculate described first in the following way Slope of the initial matching point of image and second image to place line：

K_{i} = \frac{y_{2 i} - y_{1 i}}{x_{2 i} - x_{1 i} + W_{1}}

Wherein：K_iFor slope, (x of the i-th pair initial matching point to place line_1i,y_1i)、(x_2i,y_2i) it is i-th pair initial matching Point is to, (x_1i,y_1i) for i-th initial matching point in described first image position, (x_2i,y_2i) in second image The position of i-th initial matching point, W₁Width for described first image.

10. a kind of image acquiring method, it is characterised in that include：

Using the method for the determination matching double points described in any one of claim 1～9 determine the first image and the second image Right with；

Skew between described first image and second image is determined based on the position relationship between the matching double points；

Described first image and second image are determined according to the skew between described first image and second image Overlapping region；