CN113068015A - Endoscope image distortion correction system based on optical fiber probe - Google Patents
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Abstract
The invention discloses an endoscope image distortion correction system based on an optical fiber probe, which comprises an optical fiber probe collector, a control host, a light source device and a display, wherein an endoscope original image is obtained according to a front end optical fiber probe collector and is transmitted to the control host through a video line; when the endoscope image distortion correction system based on the optical fiber probe is used, the image position of a specific area is selected, and characteristic correction is performed one by one, so that the obtained image is clearly displayed, the color of the specific area is enhanced by using an image detection unit designed in the distortion correction system, and the accuracy of the system for detecting the image is improved.
Description
Technical Field
The invention belongs to the technical field of endoscope image processing, and particularly relates to an endoscope image distortion correction system based on an optical fiber probe.
Background
The electronic endoscope is a medical electronic optical instrument integrating high-precision technologies such as light collection, machine and electricity, and can be inserted into the body cavity and the internal cavity of an internal organ of a human body to perform direct observation, diagnosis and treatment.
When the existing endoscope is used, a plurality of images are randomly selected from the images to carry out image identification, alignment and pseudo-color assignment, and finally a color image is formed.
Disclosure of Invention
The invention aims to provide an endoscope image distortion correction system based on a fiber probe, which solves the problems that the image correction system has large accuracy deviation and low image acquisition correction rate when the existing endoscope proposed in the background technology is used.
In order to achieve the purpose, the invention adopts the following technical scheme: an endoscope image distortion correction system based on an optical fiber probe comprises an optical fiber probe collector, a control host, a light source device and a display, wherein an endoscope original image is obtained according to a front end optical fiber probe collector, the endoscope original image is transmitted to the control host through a video line, the control host corrects a deviation range which can be generated in other images in a series of endoscope original images in a specific area to generate a corresponding endoscope correction image, the endoscope correction image is transmitted to the display through another video line, the light source device is connected with the optical fiber probe collector through a light guide beam, the control host comprises an image correction unit, an image detection unit and a data transmission unit, the image correction unit is used for performing correction identification of the deviation range on the obtained endoscope original image and transmitting the corrected image to the display through the data transmission unit, and the image detection unit is used for the correction identification of the foreign matter part in the endoscope correction image.
Furthermore, the offset range which can be generated in other images in the original image of the endoscope is the pixel offset range which is generated in the specific area based on a plurality of pre-calibrated pixel points, the specific area obtains the pixel offset range of at least three pre-calibrated pixel points, the tangent line at the outermost side of the pixel offset range is intersected to form a closed area which is used as the offset range, the at least three pixel points comprise three pixel points positioned on the boundary of the specific area, the image detection unit detects a foreign body part through the endoscope correction image formed by the image correction unit, performs color enhancement identification on the detected foreign body part, obtains an enhanced image model of the foreign body part, and the specific area refers to a certain area which is manually specified in the target image; adjusting the optical fiber probe collector to enable the target object to fall into a preset area when shooting the target image; or automatically identifying the area where the target object is located in the target image, carrying out distortion correction on the specific area of the target image and the offset range of the image to be aligned by the image correction unit, and after finishing image alignment, converting the alignment mapping relation based on the distortion coefficient to the position before distortion correction.
Furthermore, color enhancement recognition in the image detection unit takes the original image as guide filtering of a guide graph to separate a brightness layer and a detail layer of each channel in the image, a neural network unit is further arranged in the control host and used for predicting the original image of the low-quality endoscope and the original image of the non-low-quality endoscope, and the neural network unit filters the acquired original image of the non-low-quality endoscope to recognize organ parts and transmits organ information of the recognized original image of the target endoscope to a display.
Compared with the prior art, the invention has the beneficial effects that:
1. when the endoscope image distortion correction system based on the optical fiber probe is used, the image position of a specific area is selected, and characteristic correction is performed one by one, so that the obtained image is clearly displayed, the color of the specific area is enhanced by using an image detection unit designed in the distortion correction system, and the accuracy of the system for detecting the image is improved.
2. By means of the pixel point contrast correction in the system, the situation that the observation result is affected due to large chromatic aberration which often occurs in the traditional assignment correction is avoided, and the brightness layer and the detail layer of each channel in the image are separated by the guide wave, so that the observation result is clearer.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a schematic diagram of the overall flow structure of the present invention;
FIG. 2 is a schematic view of the overall structure of the present invention;
FIG. 3 is a schematic diagram of a comparative structure of images acquired before and after use in the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
In the description of the present invention, it is to be understood that the terms "upper", "lower", "front", "rear", "left", "right", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
Example one
Referring to fig. 1 to 3, the present invention provides a technical solution: an endoscope image distortion correction system based on an optical fiber probe comprises the following steps that when a control host receives a collection instruction corresponding to a first sampling image, the first sampling image is displayed and recorded as an endoscope original image, the endoscope original image is transmitted to the control host through a video line, the original image is recorded in the same way, when the control host receives a correction instruction, the currently displayed sampling image in at least one sampling image is determined as a target sampling image for distortion correction, the offset range which can be generated in other images in a series of endoscope original images in a specific area is corrected, a corresponding endoscope correction image is generated, the endoscope correction image is transmitted to a light guide display through another video line, a light source is connected with an optical fiber probe collector through a light beam, wherein if the shooting direction of each target sampling image comprises all preset shooting directions, and determining distortion correction parameters of the lens of the camera to be detected according to the target sampling image and a preset distortion correction algorithm.
In this embodiment, further, the offset range that can be generated in another image in the original image of the endoscope is based on the pixel offset range generated in the specific region by the plurality of pre-calibrated pixel points, the specific region obtains the pixel offset range of at least three pre-calibrated pixel points by obtaining the pixel offset range of the at least three pre-calibrated pixel points, the closed region formed by intersecting the tangents at the outermost sides of the pixel offset range is used as the offset range, the local features of each image in the image sequence are obtained, and feature point matching is performed on each image in the image sequence based on the local features of each image, so as to obtain a matching result; and when the matching result is that the number of the feature points matched with the first image and the second image is larger than or equal to a preset threshold value, taking the first image as a key frame, taking the first image and the second image as any two adjacent frames of images in the image sequence, searching the feature points in the specific area of the target image and the offset range of the image to be aligned and matching the feature point pairs, and calculating the alignment mapping relation from the image to be aligned to the target image based on the feature point pairs.
In this embodiment, further, the image detection unit detects the foreign object portion through the endoscope correction image formed by the image correction unit, and performs color enhancement identification on the detected foreign object portion, to obtain an enhanced image model of the foreign object portion, for example, the absorption coefficient of the red light band is very small compared to the blue and green bands, and the penetration capability of the red light band is strongest, which can reach the submucosa layer, the blue light and the green light can be absorbed by the mucosa and the blood vessel, the penetration depth can only reach the mucosa layer, and the absorption capability of the hemoglobin to the blue light and the green light is far stronger than the mucosa layer and the epithelial layer. And the absorption of hemoglobin in blue and green wave band reaches the peak, when the light shines various organs, the blue wave band penetrates the epithelial layer, reaches the mucous membrane shallow layer and is reflected promptly, and the blood vessel in the shallow layer absorbs the blue wave band, therefore contains the abundant information of blood vessel in the mucous membrane shallow layer in the endoscope image, and green light can reach the middle level of mucous membrane, is all absorbed by the fine blood vessel in middle level, and the red wave band penetrates epithelial layer and mucous membrane layer, is absorbed by the blood vessel in the mucous membrane lower floor.
In the endoscope image, the blue component and the green component are improved, the red component is reduced to change the tone of the tissue background and the blood vessel, so that the blood vessel and the background visually meet the contrast color, the blood vessel contrast is enhanced, the evaluation method of the detail background variance ratio is to divide the pixels of the image into background pixels and foreground pixels, and the definition of the background pixels is as follows: when the variance of the neighborhood gray value of the pixel point is smaller than a set threshold value T, the pixel point is considered as a background pixel; the definition of the foreground pixel is opposite to that of the foreground pixel, and the foreground pixel is called when the neighborhood gray-scale value of the pixel point is larger than the threshold value T.
Wherein, in the process of processing the endoscope original image, the original image is represented by a function f (x, y), the linear bit invariant factor is h (x, y), and the convolution result is an output image g (x, y), then:
then according to the convolution theorem, there are:
G(u，v)＝H(u，v)F(u，v)
note that in the formula, G (u, v), H (u, v), and F (u, v) are fourier transforms of G (x, y), H (x, y), and F (x, y), respectively. And, H (u, v) is referred to as a transfer function. Usually f (x, y) is a known original image function, then g (x, y) can be obtained only by knowing the transfer function H (u, v); or only the transfer function H (u, v) needs to be changed to control the output image. The output image g (x, y) is:
g(x，y)＝T1[H(u，v)F(u，v)]
the image enhancement of the frequency domain is mainly realized by filtering the image by a filter, and H (u, v) represents the transfer function of the filter, which can be regarded as the filter, and different filters can be designed and selected for different images and enhancement requirements, and beta of each channelR，βG，βBDetermining parameters, defining a target with a maximum hue distance
Max(Ben，Ven/Bori，Vori)
As hereinbefore described Ben，VenRespectively enhanced image EcBackground region and blood vessel region in (1), Bori，VoriRespectively, is the original image IcBackground area and vessel area in (1). The specific steps of selecting m-20 and n-218 and specifically calculating beta are as follows:
s1: inputting an ith image, wherein the size of the image is 128 multiplied by 128, and if i is larger than m, ending the calculation and jumping to S8); otherwise, executing S2);
s2: inputting a jth group of parameters, and jumping to S7 if j is larger than n; otherwise, executing S3;
s3: processing the input image by using a contrast enhancement algorithm;
s4: selecting a blood vessel area and a background area of an original image and an enhanced image;
s5: converting the image from the RGB space to the CIE space, wherein the conversion formula is as follows:
X＝2.7690R+1.7518G+1.1300B；
wherein:
Y＝1.0000R+4.5907G+0.0601B；
Z＝0.0000R+0.0565G+5.5943B；
s6: respectively calculating the distance B between the background and the blood vessel region of the original image and the processed imageori，VoriAnd Ben，VenThen save the value to array VdisMiddle, and go back to S2;
s7: according toThe tone distance maximization target can obtain a set of optimal parameters beta of the ith imageR,βG,βB. Store the parameters in V separatelyecR,VecG,VecBAnd jumps back to S1;
s8: to VecR,VecG,VecBRespectively carrying out mean values to obtain a group of optimal parameters betaRbest,βGbest,βBbestAnd finishing the calculation to finish the steps.
In this embodiment, further, the offset range that can be generated in another image in the original image of the endoscope is based on the pixel offset range generated in the specific area by the plurality of pre-calibrated pixel points, the specific area obtains the pixel offset range of at least three pre-calibrated pixel points, the tangent line at the outermost side of the pixel offset range intersects to form a closed area as the offset range, and the at least three pixel points include three pixel points located on the boundary of the specific area, wherein the image detection unit detects the foreign object portion through the endoscope correction image formed by the image correction unit, performs color enhancement identification on the detected foreign object portion, obtains an enhanced image model of the foreign object portion, and the specific area refers to a certain area manually specified in the target image; adjusting the optical fiber probe collector to enable the target object to fall into a preset area when shooting the target image; or automatically identifying the area where the target object is located in the target image, carrying out distortion correction on the specific area of the target image and the offset range of the image to be aligned by the image correction unit, and after finishing image alignment, converting the alignment mapping relation based on the distortion coefficient to the position before distortion correction.
Example two
Referring to fig. 1 to 3, the present invention provides a technical solution: an endoscope image distortion correction system based on an optical fiber probe comprises an optical fiber probe collector, a control host, a light source device and a display, acquiring an original image of the endoscope according to a front-end optical fiber probe collector, transmitting the original image of the endoscope to a control host through a video line, correcting the offset range which can be generated in other images in a series of original images of the endoscope in a specific area by the control host to generate a corresponding corrected image of the endoscope, and transmits the endoscope correction image to the display through another video line, the light source is connected with the optical fiber probe collector through the light guide beam, the correction part in the acquired endoscope original image is identified in all directions through a color enhancement identification part arranged in an image detection unit, and the identification part is directly calibrated and displayed to obtain a corresponding endoscope correction image. Such an identification system is simple in operation mode, intersects with a classification distinguishing identification mode used in a mixed mode, and can acquire a clear image in a wider area, for example, as follows:
dividing the original image of the endoscope collected by the optical fiber probe collector into RGB format, and adjusting the image channels into gray scale mode, wherein the channel of each image is represented as Ic(x, y) and carrying out the reaction with Ic(x, y) guided filtering is used as a guided mapping to obtain a luminance layer L of the acquired imagec(x, y) wherein c represents R, G and B channel, fguidfilterFor the guided filter function, the calculation formula is derived as follows:
Lc(x，y)＝fguidfilter(Ic(x，y)，
then multiplying the brightness layer obtained by the calculation of the formula by a stretching coefficient beta to obtain a brightness stretching processing image of each channel, and then carrying out image Ic(x, y) minus Lc(x, y), thereby obtaining a detail layer image Dc(x, y) which is calculated as follows:
Dc(x，y)＝Ic(x，y)-Lc(x，y)。
the structural features of blood vessels, mucosa and the like are all in the layer, finally, the detail layer is multiplied by the gain coefficient alpha to obtain a detail enhanced image, and the brightness stretching is added to the detail enhanced image to obtain an enhanced image E after the image is processedc(x, y), the calculation formula is as follows:
Ec(x，y)＝βc×Lc(x，y)+αc×Dc(x，y)。
it is noted that, as the gain factor α is higher, the image is enhanced more strongly, as can be derived from the above calculation formula for enhancing the image. However, the enhancement is too high, which may cause the enhanced image to have artifacts, so the optimal calculation formula is proposed as follows:
Ac＝10×SNR(Ic(x,y))，
the SNR is the signal-to-noise ratio and the artifact ratio of the image, and is obtained by a direct proportion function, and the higher the signal-to-noise ratio and the artifact ratio of the image is, the more obvious the enhancement is. The detail enhancement evaluation method is to divide the pixels of the image into background pixels and foreground pixels, and the differentiation mode of the background pixels of the image is as follows:
when the variance of the neighborhood gray value of the pixel point is smaller than a set threshold value T, the pixel point is considered as a background pixel;
when the neighborhood gray scale value of the pixel point is larger than the threshold value T, the pixel is called a foreground pixel.
And the local variance of the background colors before and after the detail enhancement proportion is calculated by using a 5 multiplied by 5 sliding window, then the specific value of the threshold value T is set, and the variance result is calculated by using the local variance mean of the detail area pixels.
By means of the steps, the G component and the B component of the image are enhanced, the R component is correspondingly reduced, the obvious contrast effect can be generated on the tone of the tissue background and the tone of blood vessels and other tissue cells, the original image is used as the guide filtering of the guide image, the brightness layers of all channels are separated, the detail layer containing the blood vessel characteristics is enhanced, the G component and the B component of the whole image are increased by stretching the brightness layers, the R component is reduced, and the contrast of the image is further enhanced.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (10)
1. An endoscope image distortion correction system based on a fiber-optic probe is characterized in that: the system comprises an optical fiber probe collector, a control host, a light source device and a display, wherein the endoscope original image is acquired by the front end optical fiber probe collector and is transmitted to the control host through a video line, the control host corrects the offset range which can be generated in other images in a series of endoscope original images in a specific area to generate a corresponding endoscope correction image, and the endoscope correction image is transmitted to the display through another video line.
2. The system of claim 1, wherein: and the light source device is connected with the optical fiber probe collector through the light guide beam.
3. The system of claim 1, wherein: the control host comprises an image correction unit, an image detection unit and a data transmission unit, wherein the image correction unit is used for carrying out correction identification on the offset range of the acquired endoscope original image and transmitting the corrected image to the display through the data transmission unit, and the image detection unit is used for carrying out correction identification on the foreign matter part in the endoscope correction image.
4. The system of claim 3, wherein: the method comprises the steps that the offset range which can be generated in other images in an original image of the endoscope is a pixel offset range which is generated in a specific area based on a plurality of pre-calibrated pixel points, the specific area obtains the pixel offset ranges of at least three pre-calibrated pixel points, a closed area which is formed by intersecting tangent lines at the outermost sides of the pixel offset ranges is used as the offset range, and the at least three pixel points comprise three pixel points which are located on the boundary of the specific area.
5. The system of claim 3, wherein: the image detection unit detects the foreign body part through the endoscope correction image formed by the image correction unit, performs color enhancement identification on the detected foreign body part, and acquires an enhanced image model of the foreign body part.
6. An endoscopic image distortion correction system based on a fiber optic probe as claimed in claim 5, characterized in that: and the color enhancement in the image detection unit identifies guide filtering taking the original image as a guide graph so as to separate the brightness layer and the detail layer of each channel in the image.
7. The system of claim 1, wherein: and a neural network unit is also arranged in the control host, and is used for predicting the low-quality endoscope original image and the non-low-quality endoscope original image.
8. The system of claim 7, wherein: the neural network unit filters the acquired non-low-quality endoscope original image to perform organ part identification, and transmits organ information of the identified target endoscope original image to the display.
9. The system of claim 4, wherein: the specific area refers to a certain area manually specified in the target image; adjusting the optical fiber probe collector to enable the target object to fall into a preset area when shooting the target image; or automatically identifying the area of the target object in the target image.
10. The system of claim 3, wherein: and the image correction unit carries out distortion correction on the specific area of the target image and the offset range of the image to be aligned, and converts the alignment mapping relation into the image before distortion correction based on the distortion coefficient after the image alignment is finished.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113545735A (en) * | 2021-09-18 | 2021-10-26 | 广州永士达医疗科技有限责任公司 | OCT image display adjustment method and device |
| CN113570520A (en) * | 2021-07-28 | 2021-10-29 | 苏州微景医学科技有限公司 | Optical fiber image correction method, device and computer readable storage medium |
| CN113837973A (en) * | 2021-11-29 | 2021-12-24 | 中国科学院苏州生物医学工程技术研究所 | Confocal endoscope image correction method and system based on optical fiber probe |
| CN115661122A (en) * | 2022-11-14 | 2023-01-31 | 南京图格医疗科技有限公司 | Method and system for removing image grid lines |
| CN115861147A (en) * | 2023-03-01 | 2023-03-28 | 广东欧谱曼迪科技有限公司 | Endoscope dark area enhancing method and device, electronic equipment and storage medium |
| WO2024208117A1 (en) * | 2023-04-03 | 2024-10-10 | 无锡海斯凯尔医学技术有限公司 | Real-time correction method and apparatus for optical-fiber image, and storage medium and electronic device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190053693A1 (en) * | 2017-08-17 | 2019-02-21 | Sony Olympus Medical Solutions Inc. | Endoscope system |
| CN110490856A (en) * | 2019-05-06 | 2019-11-22 | 腾讯医疗健康(深圳)有限公司 | Processing method, system, machinery equipment and the medium of medical endoscope image |
| CN110533612A (en) * | 2019-08-27 | 2019-12-03 | 中山大学 | Imaging method, device, equipment and the medium of endoscopic images |
| CN111161852A (en) * | 2019-12-30 | 2020-05-15 | 北京双翼麒电子有限公司 | Endoscope image processing method, electronic equipment and endoscope system |
-
2021
- 2021-03-24 CN CN202110316211.6A patent/CN113068015A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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