CN108269261A - A kind of Bones and joints CT image partition methods and system - Google Patents

A kind of Bones and joints CT image partition methods and system Download PDF

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CN108269261A
CN108269261A CN201611263197.3A CN201611263197A CN108269261A CN 108269261 A CN108269261 A CN 108269261A CN 201611263197 A CN201611263197 A CN 201611263197A CN 108269261 A CN108269261 A CN 108269261A
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class
bony areas
ellipsoid
images
image
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王瑜
张晓璐
高翔
温涛
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Bright Oceans Inter Telecom Co Ltd
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Bright Oceans Inter Telecom Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30008—Bone

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Abstract

The application provides a kind of Bones and joints CT image partition methods and system, by being pre-processed to the CT images got, obtains the bony areas in the CT images；Class elliptic region positioning is carried out to obtained each bony areas, obtains being imaged as the bony areas of class ellipsoid；According to preset rules by selecting at least one class ellipsoid in obtained each class ellipsoid, the class ellipsoid of selection is labeled as seed region；It is background area by non-bone zone marker by the seed region labeled as target area.Using the energy smallest partition algorithm based on graph theory, by the target area and background area of above-mentioned setting, the bony areas for being connected to one piece in joint is separated, and different marks is set to different bony areas.The automatic segmentation of CT images is realized, improves the efficiency of CT images segmentation.

Description

A kind of Bones and joints CT image partition methods and system
Technical field
The present invention relates to technical field of image processing, and in particular to a kind of Bones and joints CT image partition methods and system.
Background technology
It is largely needed both at home and abroad for Bones and joints CT image three-dimensionals volume segmentation method in each CT sequence chart at present It is manuallyd locate or manual segmentation as in, it is time-consuming and laborious, and efficiency is low.Therefore be badly in need of one kind be used to implement to arthrosis image into The dividing method of row Fast Segmentation.
Invention content
In view of this, the embodiment of the present invention provides a kind of Bones and joints CT image partition methods and system, to realize Bones and joints The automatic segmentation of CT images.
To achieve the above object, the embodiment of the present invention provides following technical solution：
A kind of Bones and joints CT image partition methods, including：
To the CT images got, the CT images are pre-processed, obtain the bony areas in the CT images；
Class elliptic region positioning is carried out to obtained each bony areas using Hough transform, obtains being imaged as class The bony areas of ellipsoid；
At least one class ellipsoid is selected from obtained each class ellipsoid according to preset rules, the class of selection is oval Face is labeled as seed region；
It is background area by non-bone zone marker by the seed region labeled as target area；
It, will even by the target area and background area of above-mentioned label using the energy smallest partition algorithm based on graph theory It is connected on one piece of bony areas to separate in joint, and different marks is set to different bony areas.
Preferably, in above-mentioned Bones and joints CT image partition methods, the pretreatment includes：Including：
The CT images got are positioned, obtain the human region in image；
Noise reduction process is carried out to the human region that positioning obtains；
Bony areas positioning is carried out to the human region after noise reduction process and removes soft tissue area, obtains the CT images In bony areas.
Preferably, in above-mentioned Bones and joints CT image partition methods, the bony areas for being connected to one piece is separated in joint, And it after different marks is set to different bony areas, further includes：
The boundary of different bony areas is optimized.
Preferably, it is described oval by obtained each class according to preset rules in above-mentioned Bones and joints CT image partition methods At least one class ellipsoid is selected in face, including：
According to formula S=a1x1+a2x2+a3x3+a4x4+a5x5+a6x6The score value S of each class ellipsoid is calculated, will divide The highest one or more class ellipsoids of value S are labeled as seed region；
Wherein, the x1For the edge gray scale of class ellipsoid and center gray value ratio, the x2Major and minor axis for class ellipsoid Than x3For the area of class ellipsoid, x4For the long axis of class ellipsoid and the angle of horizontal plane, x5For class ellipsoid central point with The distance at target area center, the target area are according to the bony areas in binary-state threshold method labeling CT image, x6For The boundary of class ellipsoid and the distance of soft tissue boundary line, the soft tissue boundary line are gray scale in the image greyscale histogram It is worth the boundary formed in the bony areas for the position of preset value；The a1、a2、a3、a4、a5、a6Respectively predetermined coefficient.
Preferably, it in above-mentioned Bones and joints CT image partition methods, further includes：Obtain the highest classes of score value S input by user The true score value S ' of ellipsoid, according to the true score value S ', x1、x2、x3、x4、x5And x6To formula S=a1x1+a2x2+a3x3+ a4x4+a5x5+a6x6It is trained, a is adjusted according to training result1、a2、a3、a4、a5And a6Size.
A kind of Bones and joints CT image segmentation systems, including：
Pretreatment unit, for the CT images got, being pre-processed to the CT images, obtaining the CT images In bony areas；
Class elliptic region positioning unit, it is ellipse for carrying out class to obtained each bony areas using Hough transform Circle zone location obtains being imaged as the bony areas of class ellipsoid；
Seed region positioning unit, for selecting at least one class from obtained each class ellipsoid according to preset rules The class ellipsoid of selection is labeled as seed region by ellipsoid；
Indexing unit, for labeled as target area, the bony areas corresponding to the seed region will to be removed the bone Zone marker other than bone region is background area；
Indexing unit, for using the energy smallest partition algorithm based on graph theory is utilized, passing through the target area of above-mentioned label Domain and background area separate the bony areas for being connected to one piece in joint, and different to different bony areas settings Mark.
Preferably, in above-mentioned Bones and joints CT image segmentation systems, the pretreatment unit is specifically configured to：
Position the human region in the CT images got；Noise reduction process is carried out to the human region that positioning obtains；To drop Making an uproar treated, human region carries out bony areas positioning and removes soft tissue area, obtains the bone area in the CT images Domain.
Preferably, it in above-mentioned Bones and joints CT image segmentation systems, further includes：
Optimize unit, for being optimized to the bony areas for setting different marks into row bound.
Preferably, in above-mentioned Bones and joints CT image segmentation systems, the seed region positioning unit is specifically configured to：
According to formula S=a1x1+a2x2+a3x3+a4x4+a5x5+a6x6The score value S of each class ellipsoid is calculated, will divide The highest one or more class ellipsoids of value S are labeled as seed region；
Wherein, the x1For the edge gray scale of class ellipsoid and center gray value ratio, the x2Major and minor axis for class ellipsoid Than x3For the area of class ellipsoid, x4For the long axis of class ellipsoid and the angle of horizontal plane, x5For class ellipsoid central point with The distance of target area central point, the target area are according to the bony areas in threshold method labeling CT image, x6It is ellipse for class The boundary of disc and the distance of soft tissue boundary line, the soft tissue boundary line is that gray value is in the image greyscale histogram The boundary that the position of preset value is formed in the bony areas；The a1、a2、a3、a4、a5、a6Respectively predetermined coefficient.
Preferably, it in above-mentioned Bones and joints CT image segmentation systems, further includes：
Formula optimization unit, for obtaining the true score value S ' of the highest class ellipsoids of score value S input by user, according to institute State true score value S ', the x of the highest class ellipsoids of score value S1、x2、x3、x4、x5And x6To formula S=a1x1+a2x2+a3x3+a4x4+ a5x5+a6x6It is trained, a is adjusted according to training result1、a2、a3、a4、a5And a6Size.
The application provides a kind of Bones and joints CT image partition methods and system, by being located in advance to the CT images got Reason, obtains the bony areas in the CT images；Class elliptic region positioning is carried out to obtained each bony areas, is obtained It is imaged as the bony areas of class ellipsoid；Select at least one class oval from obtained each class ellipsoid according to preset rules The class ellipsoid of selection is labeled as seed region by face；By the seed region labeled as target area, by non-bony areas mark It is denoted as background area.Using the partitioning algorithm based on graph theory, by the target area and background area of above-mentioned setting, will be connected to One piece of bony areas is separated in joint, and different marks is set to different bony areas.Realize oneself of CT images Dynamic segmentation improves the efficiency of CT images segmentation.
Automatic precision refinement segmentation is realized using the energy smallest partition algorithm based on graph theory, take full advantage of in three dimensions The algorithmic characteristic using energy minimization path as boundary, can reach satisfied segmentation effect to a certain extent.
And in system in actual use, can by inputting training sample, based on self-learning algorithm adjustment formula S= a1x1+a2x2+a3x3+a4x4+a5x5+a6x6In predetermined coefficient a1、a2、a3、a4、a5、a6So that the accuracy of seed region positioning It is continuously improved, so as to improve the precision of segmentation.
Description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, to embodiment or will show below There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this The embodiment of invention, for those of ordinary skill in the art, without creative efforts, can also basis The attached drawing of offer obtains other attached drawings.
Fig. 1 is a kind of flow diagram of Bones and joints CT image partition methods disclosed in the embodiment of the present application；
Fig. 2 is the internal anatomy of hip joint；
Fig. 3 is basin bone CT images；
Fig. 4 is label human region image；
Fig. 5 is label bone region image；
Fig. 6 is the image of class ellipse fitting positioning seed region；
Fig. 7 is GraphCut algorithm flow charts；
Fig. 8 is last segmentation result label figure；
Fig. 9 is segmentation result three-dimensional reconstruction design sketch；
Figure 10 is a kind of image greyscale histogram schematic diagram of bony areas；
Figure 11 is a kind of structure diagram of Bones and joints CT image segmentation systems disclosed in the embodiment of the present application.
Specific embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, those of ordinary skill in the art are obtained every other without making creative work Embodiment shall fall within the protection scope of the present invention.
CT is that the certain thickness level in human body portion is scanned with X-ray beam, is received by detector through the level X ray, after being changed into visible ray, electric signal is become, then through analog/digital converter (analog/ from opto-electronic conversion Digital converter) switch to digital signal, input computer disposal.The processing that image is formed is like to selected level point The cuboid identical into several volumes, referred to as voxel (voxel).Different tissues also has in CT imagings upper density and compares Big difference, atmospheric density is than relatively low under normal circumstances, and secondly, blood vessel is higher, bone or calcified tissue's density highest for fat.
CT images joint segmentation refer to come out joint separate marking in CT images, be mainly used for it is preoperative planning or Person's lesion analysis etc., such as hip joint refer to coming out hip joint separate marking in CT images.
Referring to Fig. 1, this application discloses a kind of Bones and joints CT image partition methods, to realize to Bones and joints in CT images Automatic segmentation, such as the segmentation in the CT images of hipbone joint to hip joint, referring to Fig. 1, this method includes：
Step S101：Obtain CT images；
In this step, the CT images got can be the scan image of a sequence as shown in Figure 3, each sequence Can include 200-300 CT images；
Step S102：The CT images got are pre-processed, obtain the bony areas in the CT images；
Wherein, the bony areas may refer to shown in Fig. 5, wherein, the region marked as 1 in described Fig. 5 can generation Table bony areas, can be by bony areas labeled as pre-set color, for example, by Fig. 5 in order to distinguish bony areas and other regions Zone marker marked as 1 is red.
Step S103：Class elliptic region positioning is carried out to obtained each bony areas, obtains being imaged as class ellipse The bony areas in face；
Referring to Fig. 2, based on synosteology structure, particularly ball and socket joint almost spherical feature, it is in the axial face of bone Reveal the elliptical shape of class.It, can be adjacent by detecting the class ellipsoid realization referring to the bony areas for being is marked in Fig. 6 Bony areas is split as multiple independent sub- bony areas by the differentiation between bone, and each independent sub- bony areas is The bony areas being made of one piece of complete bone；
Step S104：It is selected from obtained each class ellipsoid using Hough transform foundation preset rules at least one Class ellipsoid, by the class ellipsoid of selection labeled as seed region, referring to Fig. 6, the seed region in Fig. 6 is the area labeled as 2 Domain in order to which seed region and other bony areas are distinguished, can be used and the seed region is carried out using special marking Seed region marked as 2 is labeled as green by label, such as Fig. 6, and red is labeled as labeled as 1 bony areas；
In view of femoral head or ilium the elliptical characteristic of class can be also showed in axial face some regions, it is therefore desirable to from Specific class elliptic region is selected in all elliptic regions detected as seed region, so that the seed that positioning obtains Region is the corresponding bony areas of target bone, and when needing to select different bones as target area, it is right with it only to select The preset rules answered.
In the class elliptic region in obtaining each CT images, Hough transform can be used and go to detect in all CT images Class ellipsoid.It is contemplated that the difference of sample bone difference and CT equipment, directly with Hough transform, it is difficult to accurately examine Measure class ellipsoid.Especially when bone is in the case where there is lesion, accuracy rate becomes lower.Therefore this method is proposed with wide Adopted Hough transform goes to detect such elliptic region.Using Generalized Hough Transform on the one hand in detection speed than directly using Hough transform is faster, the occurrence of on the other hand can also effectively reducing missing inspection.
Step S105：It is background area by non-bone zone marker by the seed region labeled as target area；
In this step, referring to Fig. 6, emphasis label is carried out using the seed region as the target area to be observed, with So that it has significant difference with other regions；
Step S106：Referring to Fig. 7, using the partitioning algorithm based on graph theory, target area and background by above-mentioned setting Region separates the bony areas for being connected to one piece in joint, and different marks is set to different bony areas；
In this step, after label obtains the target area, the CT is preferably individually observed in order to facilitate user Each bony areas being made of independent bone in image needs to separate each adjacent bony areas in this step Come, this step is split the bony areas in image, will be connected to by cutting algorithm (GraphCuts) based on graph theory One piece of bony areas is separated in joint.GraphCuts algorithms are the gray value and coordinate using CT images, and structure one has Vertex and the collection of illustrative plates on side set area-of-interest and background area, using the principle of energy minimum, energy minimization path are made For boundary, collection of illustrative plates is divided into the process of area-of-interest and background area.
Referring to Fig. 8, when corresponding bony areas (seed region) label of a targeted bone in the CT images is completed Afterwards, method disclosed in performing the above embodiments of the present application is repeated, is continued to the corresponding seed region of bones other in CT images It is marked, certainly, the differentiation of different seed regions for convenience, the mark of each seed region is different, such as in Fig. 8, kind Subregion 2 uses red-label, and seed region 3 uses light blue color marker, and seed region 4 uses Green Marker, and seed region 5 is adopted With dark blue color marker, and use referring to Fig. 9 the segmentation result effect of each seed region of Surface Rendering Three-dimensional Displays Figure.
In above-mentioned technical proposal disclosed in the embodiment of the present application, the pretreatment can include
A1：Positioned to obtain the human region in image to the CT images got；
A2：Noise reduction process is carried out to the human region that positioning obtains；
A3：Bony areas positioning is carried out to the human region after noise reduction process and removes soft tissue area, obtains the CT Bony areas in image.
Wherein, when positioning human region, process can be：
By locating points of interest in CT images, each connected region of point of interest in CT images is obtained, and retention volume is maximum Connected region；
This step is mainly to remove other interference regions such as bed body in CT images.This step can specifically pass through threshold method By positioning interest region in CT images.
The noise reduction process can be completed by Gaussian filter；
Bony areas positioning is carried out to the human region after noise reduction process and removes the detailed process of soft tissue area can be with Including：
Mark to obtain the bony areas in image according to threshold method；
By calcified tissue, process can be specially for removal:Using morphological operator removal label obtain by calcified tissue The bony areas of formation.In this process, by judge whether with the non-conterminous bony areas of other bony areas, if In the presence of, then it is believed that the bony areas be calcified tissue, cancel the label to its bony areas.
Soft tissue is removed, process is specifically as follows：Bony areas after removal calcified tissue is handled, obtains institute State the image greyscale histogram of bony areas；Gray value in image greyscale histogram is less than to the gray scale of the bony areas of preset value Value is set as in the example of 0, Figure 10 the preset value being set as 80, i.e., by gray value in image greyscale histogram less than 80 The gray value of bony areas is set as 0, during actual use, the size of the preset value can voluntarily be set according to user demand It puts.
It is described to select at least one class oval from obtained each class ellipsoid according to preset rules in the above method Face, and labeled as seed region, can specifically include：
According to formula S=a1x1+a2x2+a3x3+a4x4+a5x5+a6x6The score value S of each class ellipsoid is calculated, will divide The highest class ellipsoids of value S are labeled as seed region；The formula can be by being instructed using machine learning algorithm [such as BP neural network] It gets；
Wherein, the x1For the edge gray scale of class ellipsoid and center gray value ratio, the x2Major and minor axis for class ellipsoid Than x3For the area of class ellipsoid, x4For the long axis of class ellipsoid and the angle of horizontal plane, x5For class ellipsoid central point with The distance of target area central point, the target area are according to the bony areas in threshold method labeling CT image, x6It is ellipse for class The boundary of disc and the distance of soft tissue boundary line, the soft tissue boundary line is that gray value is in the image greyscale histogram The boundary that the position of preset value is formed in the bony areas；The a1、a2、a3、a4、a5、a6Respectively predetermined coefficient, foundation The difference of the bone of required positioning, a1、a2、a3、a4、a5、a6It is of different sizes, wherein, a1、a2、a3、a4、a5、a6's Value can according to BP neural network algorithm using corresponding bone training sample set and test sample set to formula S= a1x1+a2x2+a3x3+a4x4+a5x5+a6x6It is trained to obtain.For example, the bone when required positioning (need to close hip for hip joint Corresponding bony areas is saved as target area) when, according to BP neural network algorithm using the training sample set of hip joint and Test sample set is trained above-mentioned formula to obtain a1、a2、a3、a4、a5、a6Value, a can be obtained1Equal to 0.3, a2It is equal to 0.2, a3Equal to 0.2, a4Equal to 0.15, a5Equal to 0.1, a6Equal to 0.05.
In order to further improve the accuracy that above-mentioned formula calculates structure in actual use, may be used also in the above method To include：Training sample input by user is obtained, the training sample is put into the training sample set of corresponding bone, according to According to updated training sample set and test sample set to formula S=a corresponding to the bone1x1+a2x2+a3x3+a4x4+ a5x5+a6x6It is trained, according to a in training result update above-mentioned formula1、a2、a3、a4、a5、a6Value, wherein, the use The training sample of family input can be the training sample of the corresponding highest class ellipsoids of score value S, specially：It is defeated to obtain user The true score value S ' of the highest class ellipsoids of score value S entered, the true score value S ' can be by experts in the CT images of output Class ellipsoid scored to obtain, by the true score value S ' and its corresponding x1、x2、x3、x4、x5And x6Import training sample This set, according to training sample set again to formula S=a1x1+a2x2+a3x3+a4x4+a5x5+a6x6It is trained, according to instruction Practice result and adjust a1、a2、a3、a4、a5And a6Size.
Corresponding to the above method, disclosed herein as well is a kind of Bones and joints CT image segmentation systems, referring to Figure 11, the system It can include：
Pretreatment unit 100, is used for：To the CT images got, the CT images are pre-processed, obtain the CT Bony areas in image；
Class elliptic region positioning unit 200, for carrying out class elliptic region positioning to obtained each bony areas, Obtain being imaged as the bony areas of class ellipsoid；
Seed region positioning unit 300, for selecting at least one from obtained each class ellipsoid according to preset rules The class ellipsoid of selection is labeled as seed region by a class ellipsoid；
Zone marker unit 400, for the bony areas corresponding to the seed region labeled as target area, will to be removed Zone marker other than the bony areas is background area.
Unit 500 is identified, for utilizing the partitioning algorithm based on graph theory, target area and background area by above-mentioned setting Domain separates the bony areas for being connected to one piece in joint, and different marks is set to different bony areas.
Wherein, the pretreatment unit 100 can include, human region extraction unit, for the CT images got Positioned to obtain the human region in image；Noise reduction unit, the human region for being obtained to positioning carry out noise reduction process；Bone Bone area extracting unit for carrying out bony areas positioning to the human region after noise reduction process and removing soft tissue area, obtains To the bony areas in the CT images.
Corresponding with the above method, above system can also include boundary and optimize unit, for setting different marks Bony areas into row bound optimize.
In order to further improve the accuracy that above-mentioned formula calculates structure in actual use, in above system, also Including：
Formula optimization unit is inside configured with training sample database and test sample database, the number of training It is used to store the training sample of the advance data of user according to library, test sample database is used for the test specimens that training user pre-enters This, the formula optimization unit can also put the parameter of the highest class ellipsoids of the score value S obtained every time as training sample Enter the training sample database, certainly, due to the score value S that is calculated by formula can there are error, be put into training The true score value S ' of the training sample of sample database is input by user, when the training sample database adds new training After sample, the formula optimization unit 140 is based on BT algorithms automatically according to the true score value of the highest class ellipsoids of the score value S S’、x1、x2、x3、x4、x5And x6To formula S=a1x1+a2x2+a3x3+a4x4+a5x5+a6x6It is trained, according to training result tune The whole a1、a2、a3、a4、a5And a6Size.
For convenience of description, it is divided into various modules during description system above with function to describe respectively.Certainly, implementing this The function of each module is realized can in the same or multiple software and or hardware during application.
Each embodiment in this specification is described by the way of progressive, identical similar portion between each embodiment Point just to refer each other, and the highlights of each of the examples are difference from other examples.Especially for system or For system embodiment, since it is substantially similar to embodiment of the method, so describing fairly simple, related part is referring to method The part explanation of embodiment.System and system embodiment described above is only schematical, wherein the conduct The unit that separating component illustrates may or may not be it is physically separate, the component shown as unit can be or Person may not be physical unit, you can be located at a place or can also be distributed in multiple network element.It can root Factually border needs to select some or all of module therein realize the purpose of this embodiment scheme.Ordinary skill Personnel are without creative efforts, you can to understand and implement.
Professional further appreciates that, with reference to each exemplary unit of the embodiments described herein description And algorithm steps, can be realized with the combination of electronic hardware, computer software or the two, in order to clearly demonstrate hardware and The interchangeability of software generally describes each exemplary composition and step according to function in the above description.These Function is performed actually with hardware or software mode, specific application and design constraint depending on technical solution.Profession Technical staff can realize described function to each specific application using distinct methods, but this realization should not Think beyond the scope of this invention.
It can directly be held with reference to the step of method or algorithm that the embodiments described herein describes with hardware, processor The combination of capable software module or the two is implemented.Software module can be placed in random access memory (RAM), memory, read-only deposit Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology In any other form of storage medium well known in field.
It should also be noted that, herein, relational terms such as first and second and the like are used merely to one Entity or operation are distinguished with another entity or operation, without necessarily requiring or implying between these entities or operation There are any actual relationship or orders.Moreover, term " comprising ", "comprising" or its any other variant are intended to contain Lid non-exclusive inclusion, so that process, method, article or equipment including a series of elements not only will including those Element, but also including other elements that are not explicitly listed or further include as this process, method, article or equipment Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that Also there are other identical elements in process, method, article or equipment including the element.
The foregoing description of the disclosed embodiments enables professional and technical personnel in the field to realize or use the present invention. A variety of modifications of these embodiments will be apparent for those skilled in the art, it is as defined herein General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, it is of the invention The embodiments shown herein is not intended to be limited to, and is to fit to and the principles and novel features disclosed herein phase one The most wide range caused.

Claims (10)

1. a kind of Bones and joints CT image partition methods, which is characterized in that including：
To the CT images got, the CT images are pre-processed, obtain the bony areas in the CT images；
Class elliptic region positioning is carried out to obtained each bony areas using Hough transform, obtains being imaged as class ellipse The bony areas in face；
At least one class ellipsoid is selected from obtained each class ellipsoid according to preset rules, by the class ellipsoid mark of selection It is denoted as seed region；
It is background area by non-bone zone marker by the seed region labeled as target area；
Using the energy smallest partition algorithm based on graph theory, by the target area and background area of above-mentioned label, will be connected to One piece of bony areas is separated in joint, and different marks is set to different bony areas.
2. Bones and joints CT image partition methods according to claim 1, the pretreatment includes：Including：
The CT images got are positioned, obtain the human region in image；
Noise reduction process is carried out to the human region that positioning obtains；
Bony areas positioning is carried out to the human region after noise reduction process and removes soft tissue area, is obtained in the CT images Bony areas.
3. CT image partition methods according to claim 1, which is characterized in that closing the bony areas for being connected to one piece It separates at section, and after the mark different to different bony areas settings, further includes：
The boundary of different bony areas is optimized.
4. CT image partition methods according to claim 1, which is characterized in that described each by what is obtained according to preset rules At least one class ellipsoid is selected in a class ellipsoid, including：
According to formula S=a1x1+a2x2+a3x3+a4x4+a5x5+a6x6The score value S of each class ellipsoid is calculated, by score value S most High one or more class ellipsoids are labeled as seed region；
Wherein, the x1For the edge gray scale of class ellipsoid and center gray value ratio, the x2For the axial ratio of class ellipsoid, x3For the area of class ellipsoid, x4For the long axis of class ellipsoid and the angle of horizontal plane, x5Central point and target for class ellipsoid The distance of regional center, the target area are according to the bony areas in binary-state threshold method labeling CT image, x6It is ellipse for class The boundary of disc and the distance of soft tissue boundary line, the soft tissue boundary line is that gray value is in the image greyscale histogram The boundary that the position of preset value is formed in the bony areas；The a1、a2、a3、a4、a5、a6Respectively predetermined coefficient.
5. CT image partition methods according to claim 4, which is characterized in that further include：Obtain score value S input by user The true score value S ' of highest class ellipsoid, according to the true score value S ', x1、x2、x3、x4、x5And x6To formula S=a1x1+ a2x2+a3x3+a4x4+a5x5+a6x6It is trained, a is adjusted according to training result1、a2、a3、a4、a5And a6Size.
6. a kind of Bones and joints CT image segmentation systems, which is characterized in that including：
Pretreatment unit, for the CT images got, pre-processing, being obtained in the CT images to the CT images Bony areas；
Class elliptic region positioning unit, for carrying out class area elliptica to obtained each bony areas using Hough transform Domain positions, and obtains being imaged as the bony areas of class ellipsoid；
Seed region positioning unit, for according to preset rules by selecting at least one class oval in obtained each class ellipsoid The class ellipsoid of selection is labeled as seed region by face；
Indexing unit, for labeled as target area, the bony areas corresponding to the seed region will to be removed the bone area Zone marker other than domain is background area；
Indexing unit, for using utilizing the energy smallest partition algorithm based on graph theory, by the target area of above-mentioned label and Background area separates the bony areas for being connected to one piece in joint, and different marks is set to different bony areas.
7. Bones and joints CT image segmentation systems according to claim 6, which is characterized in that the specific quilt of the pretreatment unit It is configured to：
Position the human region in the CT images got；Noise reduction process is carried out to the human region that positioning obtains；At noise reduction Human region after reason carries out bony areas positioning and removes soft tissue area, obtains the bony areas in the CT images.
8. Bones and joints CT image segmentation systems according to claim 6, which is characterized in that further include：
Optimize unit, for being optimized to the bony areas for setting different marks into row bound.
9. Bones and joints CT image segmentation systems according to claim 6, which is characterized in that the seed region positioning unit It is specifically configured to：
According to formula S=a1x1+a2x2+a3x3+a4x4+a5x5+a6x6The score value S of each class ellipsoid is calculated, by score value S most High one or more class ellipsoids are labeled as seed region；
Wherein, the x1For the edge gray scale of class ellipsoid and center gray value ratio, the x2For the axial ratio of class ellipsoid, x3For the area of class ellipsoid, x4For the long axis of class ellipsoid and the angle of horizontal plane, x5Central point and target for class ellipsoid The distance of regional center, the target area are according to the bony areas in binary-state threshold method labeling CT image, x6It is ellipse for class The boundary of disc and the distance of soft tissue boundary line, the soft tissue boundary line is that gray value is in the image greyscale histogram The boundary that the position of preset value is formed in the bony areas；The a1、a2、a3、a4、a5、a6Respectively predetermined coefficient.
10. Bones and joints CT image segmentation systems according to claim 9, which is characterized in that further include：
Formula optimization unit, for obtaining the true score value S ' of the highest class ellipsoids of score value S input by user, according to described point True score value S ', the x of the highest class ellipsoids of value S1、x2、x3、x4、x5And x6To formula S=a1x1+a2x2+a3x3+a4x4+a5x5+ a6x6It is trained, a is adjusted according to training result1、a2、a3、a4、a5And a6Size.
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