Intelligent positioning and labeling method for anorectal lesion area

Abstract

The invention relates to the technical field of lesion area positioning of crissum MRI images, in particular to an intelligent positioning and labeling method for an anorectal lesion area. According to the method, a high-signal area in a crissum MRI image is obtained through analysis; the crissum characterization degree is obtained according to the gray scale difference of different areas and the number and gray scale distribution of pixel points in the areas, and then an initial area is screened out; obtaining a direction extension coefficient according to pixel point position distribution and gradient distribution on the texture curve in the initial region; obtaining an anorectal lesion positioning coefficient by combining the gray difference and the distance difference between each pixel point in the region and the surrounding; and the anorectal lesion area is positioned. According to the method, the anorectal lesion area in the crissum MRI image can be accurately identified, so that related personnel can better judge the anorectal lesion condition, and the misdiagnosis probability is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of lesion area positioning of anorectal images, and in particular to an intelligent positioning and labeling method for anorectal lesion areas. Background Art

[0002] MRI images are of great value in the examination of anal fistulas and have become the gold standard for preoperative evaluation in clinical practice. Its high soft tissue resolution can clearly show the direction of the fistula, the location of the internal opening, and the relationship with the anal sphincter, helping doctors to accurately classify and plan surgical plans. However, anal fistula lesions are complex, and the fistula may be tortuous and accompanied by branches. Traditional MRI examinations rely on manual interpretation, which is subjective and has the risk of missed diagnosis. Therefore, it is particularly important to develop a positioning and annotation system. Through image processing technology, it can automatically identify the lesion area, accurately locate the fistula and the internal opening, and mark them. It can not only improve the diagnostic efficiency, but also reduce human errors, providing a more reliable basis for clinical treatment, especially in the diagnosis of complex anal fistulas and recurrent anal fistulas. It has significant advantages.

[0003] MRI images are widely used to examine perianal diseases. For example, anal fistulas, especially complex multi-branch fistulas, require manual image analysis during the diagnosis process to identify high-signal areas and combine clinical experience to determine the fistula path, morphology, and fibrosis characteristics. In addition, fistulas usually appear as high-signal areas in MRI images, and these areas may be similar to other structures, such as cysts or blood vessels. The impact on subjective diagnosis will reduce diagnostic efficiency and increase the risk of misdiagnosis. Summary of the invention

[0004] In order to solve the technical problem that fistulas are usually manifested as high signal areas in MRI images, and these areas may be similar to other structures, such as cysts or blood vessels, so relevant personnel are prone to make mistakes when judging the lesion area, increasing the risk of misdiagnosis, the purpose of the present invention is to provide an intelligent positioning and marking method for anorectal lesion areas, and the technical solutions adopted are as follows: An intelligent positioning and marking method for anorectal lesion areas, the method comprising: All perianal MRI images of the patients were collected; According to the grayscale distribution of pixels in each perianal MRI image, all high-signal areas in the perianal MRI image are obtained; the edge pixels of each high-signal area are obtained; one high-signal area is selected as a reference high-signal area; according to the number of pixels and grayscale distribution in the reference high-signal area, as well as the grayscale difference between each edge pixel and the surrounding area, the perianal representation degree of the reference high-signal area is obtained; according to the perianal representation degree, all high-signal areas are screened to obtain an initial high-signal area; one initial high-signal area is selected as a target area; edge detection is performed on the target area to obtain all texture curves in the target area; according to the position distribution and gradient distribution of each pixel on the texture curve in the target area, the directional extension coefficient of the target area is obtained; according to the grayscale difference and distance difference between each pixel in the target area and other pixels in the preset neighborhood, as well as the directional extension coefficient of the target area, the anorectal lesion positioning coefficient of the target area is obtained; The anorectal lesion area is located according to the anorectal lesion localization coefficient of each initial high signal area.

[0005] Furthermore, the method for acquiring the high signal area includes: The maximum inter-class variance method was used to segment the perianal MRI images and all high signal areas were screened out from the perianal MRI images.

[0006] Furthermore, the method for obtaining the degree of perianal characterization includes: The areas other than the high signal areas in the perianal MRI images were regarded as low signal areas; The anal perianal representation degree is obtained according to the anal perianal representation degree calculation formula, and the anal perianal representation degree calculation formula is as follows: ; In the formula, It indicates the degree of perianal representation with reference to the high signal area; Represents the number of pixels within the minimum circumscribed circle of the reference high signal area; Indicates the number of pixels in the reference high signal area; Represents the standard deviation of the grayscale values ​​of pixels in the reference high signal area; Indicates the reference high signal area The gray value of each pixel; Indicates the number of edge pixels in the reference high signal area; Indicates the reference high signal area Gray value of edge pixels; The number of pixels representing low signal areas; Indicates the low signal area The gray value of a pixel.

[0007] Furthermore, the method for obtaining the initial high signal area includes: The high signal area with the perianal characterization degree greater than the preset first threshold is taken as the initial high signal area.

[0008] Furthermore, the method for obtaining the directional extension coefficient includes: A Cartesian coordinate system was established with the lower left corner of the perianal MRI image as the origin, and the image midline of the perianal MRI image perpendicular to the x-axis was selected as the reference line; Calculate the shortest distance between each pixel point in the texture curve in the target area and the reference straight line as the first distance; The directional extension coefficient is obtained according to the directional extension coefficient calculation formula. The directional extension coefficient calculation formula is as follows: ; In the formula, Indicates the directional extension coefficient of the target area; Indicates the number of texture curves within the target area; Indicates the first The number of pixels in a texture curve; Indicates the first Texture curve The gradient of each pixel; Indicates the first Texture curve The gradient of each pixel; Indicates the first Texture curve The first distance of pixels.

[0009] Furthermore, the method for obtaining the anorectal lesion positioning coefficient includes: The anorectal lesion positioning coefficient is obtained according to the anorectal lesion positioning coefficient calculation formula. The anorectal lesion positioning coefficient calculation formula is as follows: ; In the formula, Indicates the anorectal lesion localization coefficient of the target area; Indicates the directional extension coefficient of the target area; Indicates the number of pixels in the target area; Indicates the target area The distance between a pixel and the nearest pixel with the same gray value; Indicates the target area The number of other pixels in the preset neighborhood of a pixel; Indicates the target area The gray value of each pixel; Indicates the target area The preset neighborhood of pixels The grayscale value of other pixels.

[0010] Furthermore, the anorectal lesion area is located according to the anorectal lesion localization coefficient of each initial high signal area, including: Mark each initial high signal area in each perianal MRI image whose anorectal lesion localization coefficient is greater than a preset second threshold, and divide the minimum circumscribed rectangle excluding the marked initial high signal area to obtain all suspected lesion areas in each perianal MRI image; All suspected lesion areas located at the same human body position in the perianal MRI images were overlapped, and the suspected lesion area with the most overlaps was taken as the anorectal lesion area.

[0011] A system for intelligent positioning and marking of anorectal lesion areas, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the above-mentioned method for intelligent positioning and marking of anorectal lesion areas when executing the computer program.

[0012] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for intelligently locating and marking anorectal lesion areas.

[0013] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned method for intelligently locating and marking anorectal lesion areas are implemented.

[0014] The present invention has the following beneficial effects: In order to examine the perianal area of ​​the patient, the present invention first obtains all perianal MRI images of the patient; since anal fistula disease presents high signal characteristics in the perianal MRI image, all high signal areas in the perianal MRI image are located. However, there are other structural areas with higher grayscale performance near the perianal area, so the performance characteristics of the fistula disease are analyzed to obtain the perianal representation degree of the reference high signal area; since there is a large gap in the edge characteristics between other tissues with high signal characteristics and the fistula area, edge detection is first performed on each initial high signal area, so as to obtain all texture curves in each initial high signal area, and the initial high signal area is specifically judged to which tissues it belongs through the directional extension coefficient of the texture curve, and the anorectal lesion positioning coefficient of each high signal area is obtained; the anorectal lesion area is positioned according to the anorectal lesion positioning coefficient of each initial high signal area. The present invention can accurately identify the anorectal lesion area in the perianal MRI image, so that relevant personnel can better judge the situation of the anorectal lesion, thereby reducing the probability of misdiagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of a method for intelligently locating and marking anorectal lesion areas provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of the intelligent positioning and marking method of anorectal lesion area proposed by the present invention, its specific implementation method, structure, characteristics and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The specific scheme of the intelligent positioning and marking method for anorectal lesion areas provided by the present invention is described in detail below with reference to the accompanying drawings.

[0020] See also Figure 1 , which shows an intelligent positioning and marking method for anorectal lesion areas provided by an embodiment of the present invention, the method comprising: Step S1: Acquire all perianal MRI images of the patient.

[0021] The embodiment of the present invention is mainly applied to the scene of perianal disease examination of patients. In actual situations, patients usually need to lie on their backs in a scanner, and the scanner uses a body coil for imaging. During the scanning process, a T2WI sequence scan is performed on all areas of the patient's body from the lower edge of the pubic symphysis to the tip of the coccyx, and several perianal MRI images are collected by the scanner.

[0022] It should be noted that the method for acquiring perianal MRI images is a technical means well known to those skilled in the art and will not be limited or elaborated herein.

[0023] In one embodiment of the present invention, the collected perianal MRI image is denoised to minimize the noise information in the image while retaining the details and edge information in the perianal MRI image, and finally the perianal MRI image required for subsequent operations is obtained. In one embodiment of the present invention, a wavelet transform method is used for denoising, and the denoising method is not unique. It should be noted that the wavelet transform method is a technical means well known to those skilled in the art, and is not limited or elaborated herein.

[0024] Step S2: According to the grayscale distribution of pixels in each perianal MRI image, all high signal areas in the perianal MRI image are obtained; the edge pixels of each high signal area are obtained; one high signal area is selected as a reference high signal area; the perianal characterization degree of the reference high signal area is obtained according to the number of pixels and grayscale distribution of the reference high signal area, and the grayscale difference between each edge pixel and the surrounding area; all high signal areas are screened according to the perianal characterization degree to obtain an initial high signal area; one initial high signal area is selected as a target area; edge detection is performed on the target area to obtain all texture curves in the target area; the directional extension coefficient of the target area is obtained according to the position distribution and gradient distribution of each pixel on the texture curve in the target area; the anorectal lesion positioning coefficient of the target area is obtained according to the grayscale difference and distance difference between each pixel in the target area and other pixels in the preset neighborhood, as well as the directional extension coefficient of the target area.

[0025] In reality, the grayscale performance of anal fistula in perianal MRI images is more prominent, that is, it presents high signal characteristics in perianal MRI images, so the embodiment of the present invention locates all high signal areas in perianal MRI images. However, there are other structural areas with higher grayscale performance near the perianal area. For example, tissue edema may occur around the fistula due to inflammatory response, and the grayscale of the edematous area in the perianal MRI image is higher. Therefore, in the embodiment of the present invention, the performance characteristics of the fistula disease are analyzed, and the perianal characterization degree of the reference high signal area is obtained according to the number of pixels and grayscale distribution in the reference high signal area, as well as the grayscale difference between each edge pixel and the surrounding area.

[0026] Preferably, in one embodiment of the present invention, the method for acquiring the high signal area includes: The present invention adopts the OTSU threshold segmentation algorithm to select the optimal segmentation threshold for different areas in the perianal MRI image by maximizing the inter-class variance, thereby segmenting the foreground as the high signal area and the background as the low signal area. An embodiment of the present invention provides a specific method: calculate the grayscale histogram of all pixel points of the perianal MRI image, and extract the probability distribution of different gray levels therefrom, calculate the inter-class variance under different thresholds by traversing all possible thresholds, and select the threshold corresponding to the maximum inter-class variance as the segmentation threshold to complete the segmentation of the foreground and background. It should be noted that the OTSU threshold segmentation algorithm is a technical means well known to those skilled in the art, and is not limited or elaborated here.

[0027] Preferably, in one embodiment of the present invention, the method for obtaining the degree of perianal characterization includes: Since the path of the fistula can usually be divided into straight, curved or spiral types, the fistula presents obvious linear characteristics in morphological representation. Therefore, the minimum circumscribed circle that is closely related to the morphology is used to reflect the linear characteristics.

[0028] The anal perianal representation degree is obtained according to the anal perianal representation degree calculation formula, and the anal perianal representation degree calculation formula is as follows: ; In the formula, It indicates the degree of perianal representation with reference to the high signal area; Represents the number of pixels within the minimum circumscribed circle of the reference high signal area; Indicates the number of pixels in the reference high signal area; Represents the standard deviation of the grayscale values ​​of pixels in the reference high signal area; Indicates the reference high signal area The gray value of each pixel; Indicates the number of edge pixels in the reference high signal area; Indicates the reference high signal area Gray value of edge pixels; The number of pixels representing low signal areas; Indicates the low signal area The gray value of a pixel.

[0029] In the calculation formula of the perianal representation degree, the more pixels in the minimum circumscribed circle of the reference high signal area, and the fewer pixels in the reference high signal area, the more prominent the linear feature of the reference high signal area is, and the more likely the morphological feature of the reference high signal area is to be a fistula area, that is, the greater the perianal representation degree of the reference high signal area; the overall grayscale in the reference high signal area The larger the grayscale standard deviation in the area The smaller it is, the more stable the grayscale performance in the area. At this time, the reference high-signal area is more likely to be the fistula area, that is, the perianal representation degree of the reference high-signal area is greater; because low-signal fibrous tissue will appear on the fistula wall in the fistula area, the grayscale performance of this tissue in the perianal MRI image is low, so the grayscale value of the edge pixel of the fistula area is low, so when the grayscale difference between each edge pixel of the reference high-signal area and the internal pixel of the reference high-signal area The larger the grayscale difference between each edge pixel in the high signal area and the pixel in the low signal area The smaller it is, the more the edge pixel points conform to the grayscale characteristics of fibrosis. At this time, the reference high-signal area is more likely to be the fistula area, that is, the greater the degree of perianal representation of the reference high-signal area.

[0030] All high-signal areas were screened according to the degree of perianal characterization to obtain the initial high-signal area.

[0031] Preferably, in one embodiment of the present invention, the method for acquiring the initial high signal area includes: The high signal area with anal perianal characterization degree greater than the preset first threshold is taken as the initial high signal area. In one embodiment of the present invention, the preset first threshold is set to 0.8. It should be noted that the preset first threshold can be set voluntarily and is not limited here.

[0032] Because there are other tissues with high signal characteristics in the perianal MRI images, there is a big gap in the edge characteristics between these tissues and the fistula area. For example, tissue edema may occur around the fistula due to inflammatory response, but unlike the strip-like or tubular high signal of the fistula itself, the inflammatory edema will present as flake-like or floc-like high signal due to the lack of clear wall structure, and there is no clear lumen structure and blurred boundaries. The inner opening of the fistula is mostly located on the posterior midline of the dentate line, and its growth path often extends along the tissue gap or the direction of least resistance. For example, the fistula of anal fistula often spreads along the sphincter gap, that is, the potential cavity between the internal and external anal sphincters, thereby forming a specific running direction, and pus or inflammatory secretions will flow along the tissue gap to form the extension direction of the fistula. For example, if the perianal abscess is not drained in time, the pus may spread to the deep or surrounding gaps, thereby posing the risk of forming a complex multi-branch fistula. Therefore, the embodiment of the present invention first performs edge detection on each initial high signal area, thereby obtaining all texture curves in each initial high signal area, and judging which tissues the initial high signal area specifically belongs to by the direction of the texture curve.

[0033] Preferably, in one embodiment of the present invention, the method for obtaining the directional extension coefficient includes: Since the internal opening of the fistula is mostly located on the posterior midline of the dentate line, the position difference between the pixel points in each initial high-signal area and the posterior midline of the dentate line is analyzed to determine whether the area belongs to the fistula area. Therefore, a Cartesian coordinate system is established with the lower left corner of the perianal MRI image as the origin, and the image midline of the perianal MRI image perpendicular to the x-axis is selected as the reference line. At this time, the reference line is exactly the line where the posterior midline of the dentate line is located.

[0034] The shortest distance between each pixel point in the texture curve in the target area and the reference straight line is calculated as the first distance. It should be noted that calculating the shortest distance between each pixel point and the reference straight line is a technical means well known to technicians in this field and will not be elaborated here.

[0035] The directional extension coefficient is obtained according to the directional extension coefficient calculation formula. The directional extension coefficient calculation formula is as follows: ; In the formula, Indicates the directional extension coefficient of the target area; Indicates the number of texture curves within the target area; Indicates the first The number of pixels in a texture curve; Indicates the first Texture curve The gradient of each pixel; Indicates the first Texture curve The gradient of each pixel; Indicates the first Texture curve The first distance of pixels.

[0036] In the directional extension coefficient calculation formula, the average gradient difference between every two adjacent pixels in each texture curve is calculated. , if the average gradient difference is smaller, it means that the gradient direction of the texture curve is more consistent. At this time, for each texture curve Conduct analysis, The smaller it is, the larger the directional extension coefficient of the target area is; if the number of pixels in the texture curve is greater, it means that the length of the texture curve is longer. The larger the value is, the greater the directional extension coefficient of the texture curve in the target area is. All texture curves are analyzed. The larger the value is, the larger the directional extension coefficient of the target area is. Since the inner opening of the fistula is mostly located around the reference straight line, the average first distance between each pixel point in each texture curve and the reference straight line is calculated. , The smaller it is, the greater the tendency of the target area to be a fistula area, that is, the larger the directional extension coefficient of the target area.

[0037] Since the extensibility of the fistula is manifested as a gradual connection between it and the surrounding tissues, that is, the connectivity between adjacent parts of the tissues around the fistula area is strong, the perianal MRI image is positioned by combining the connectivity with the directional extension coefficient of each initial high-signal area, and the localization coefficient of anorectal lesions in the high-signal area is analyzed.

[0038] Preferably, in one embodiment of the present invention, the method for obtaining the anorectal lesion positioning coefficient includes: The anorectal lesion positioning coefficient is obtained according to the anorectal lesion positioning coefficient calculation formula. The anorectal lesion positioning coefficient calculation formula is as follows: ; In the formula, Indicates the anorectal lesion localization coefficient of the target area; Indicates the directional extension coefficient of the target area; Indicates the number of pixels in the target area; Indicates the target area The distance between a pixel and the nearest pixel with the same gray value; Indicates the target area The number of other pixels in the preset neighborhood of a pixel; Indicates the target area The gray value of each pixel; Indicates the target area The preset neighborhood of pixels The grayscale value of other pixels.

[0039] In the calculation formula of the anorectal lesion positioning coefficient, if the grayscale difference between each pixel in the target area and other pixels in the preset neighborhood is The smaller the distance between each pixel The smaller the value, the stronger the connectivity between the pixel points in the target area and the preset neighborhood. At this time, the target area is more likely to be a fistula area, that is, the anorectal lesion localization coefficient of the target area is larger; if the directional extension coefficient of the target area is The larger it is, the more likely the target area is the fistula area, that is, the greater the anorectal lesion localization coefficient of the target area.

[0040] In one embodiment of the present invention, the preset neighborhood is set to be centered on each pixel point. It should be noted that the preset neighborhood can be set by yourself and is not limited here.

[0041] Step S3: locating the anorectal lesion area according to the anorectal lesion localization coefficient of each initial high signal area.

[0042] Preferably, in one embodiment of the present invention, the specific method includes: Each initial high signal area in each perianal MRI image whose anorectal lesion localization coefficient is greater than a preset second threshold is marked, and the minimum circumscribed rectangle excluding the marked initial high signal area is divided to obtain all suspected lesion areas in each perianal MRI image; in one embodiment of the present invention, the preset second threshold can be set to 0.9. It should be noted that the preset second threshold can be set by oneself and is not limited here.

[0043] All suspected lesion areas located at the same human body position in the perianal MRI images were overlapped, and the suspected lesion area with the most overlaps was taken as the anorectal lesion area.

[0044] At this point, the intelligent positioning of the anorectal lesion area is completed.

[0045] In summary, all perianal MRI images of the patient are collected; all high signal areas in the perianal MRI images are obtained according to the grayscale distribution of pixels in each perianal MRI image; the edge pixels of each high signal area are obtained; one high signal area is selected as the reference high signal area; the perianal representation degree of the reference high signal area is obtained according to the number of pixels and grayscale distribution in the reference high signal area, as well as the grayscale difference between each edge pixel and the surrounding area; all high signal areas are screened according to the perianal representation degree to obtain the initial high signal area; one initial high signal area is selected as the target area; edge detection is performed on the target area to obtain all texture curves in the target area; the directional extension coefficient of the target area is obtained according to the position distribution and gradient distribution of each pixel on the texture curve in the target area; the anorectal lesion positioning coefficient of the target area is obtained according to the grayscale difference and distance difference between each pixel in the target area and other pixels in the preset neighborhood, as well as the directional extension coefficient of the target area; the anorectal lesion area is positioned according to the anorectal lesion positioning coefficient of each initial high signal area.

[0046] The second purpose of one embodiment of the present invention is to provide an intelligent positioning and marking system for anorectal lesion areas, the system comprising a memory, a processor and a computer program, wherein the memory is used to store the corresponding computer program, the processor is used to run the corresponding computer program, and the computer program can implement the method described in steps S1-S3 when running in the processor.

[0047] The third objective of an embodiment of the present invention is to provide a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the method described in steps S1-S3 is implemented when the processor executes the computer program.

[0048] A fourth objective of an embodiment of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in steps S1-S3 is implemented.

[0049] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0050] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. An intelligent positioning and marking method for anorectal lesion areas, characterized in that: The method comprises: All perianal MRI images of the patients were collected; According to the grayscale distribution of pixels in each of the perianal MRI images, all high-signal areas in the perianal MRI images are obtained; the edge pixels of each high-signal area are obtained; one high-signal area is selected as a reference high-signal area; according to the number of pixels and grayscale distribution in the reference high-signal area, and the grayscale difference between each edge pixel and the surrounding area, the perianal characterization degree of the reference high-signal area is obtained; according to the perianal characterization degree, all high-signal areas are screened to obtain an initial high-signal area; one initial high-signal area is selected as a target area; edge detection is performed on the target area to obtain all texture curves in the target area; according to the position distribution and gradient distribution of each pixel on the texture curve in the target area, the directional extension coefficient of the target area is obtained; according to the grayscale difference and distance difference between each pixel in the target area and other pixels in a preset neighborhood, as well as the directional extension coefficient of the target area, the anorectal lesion positioning coefficient of the target area is obtained; The anorectal lesion area is located according to the anorectal lesion localization coefficient of each initial high signal area.

2. The intelligent positioning and marking method for anorectal lesion areas according to claim 1, characterized in that: The method for acquiring the high signal area comprises: The maximum inter-class variance method is used to segment the perianal MRI image, and all high signal areas are screened out from the perianal MRI image.

3. The intelligent positioning and marking method for anorectal lesion area according to claim 1, characterized in that: The method for obtaining the anal perianal characterization degree comprises: The areas other than the high signal areas in the perianal MRI images were regarded as low signal areas; The anal perianal characterization degree is obtained according to the anal perianal characterization degree calculation formula, and the anal perianal characterization degree calculation formula is as follows: ； In the formula, It indicates the degree of perianal representation with reference to the high signal area; Represents the number of pixels within the minimum circumscribed circle of the reference high signal area; Indicates the number of pixels in the reference high signal area; Represents the standard deviation of the grayscale values ​​of pixels in the reference high signal area; Indicates the reference high signal area The gray value of each pixel; Indicates the number of edge pixels in the reference high signal area; Indicates the reference high signal area Gray value of edge pixels; The number of pixels representing low signal areas; Indicates the low signal area The gray value of a pixel.

4. The intelligent positioning and marking method for anorectal lesion area according to claim 1, characterized in that: The method for acquiring the initial high signal area comprises: The high signal area with the perianal characterization degree greater than the preset first threshold is taken as the initial high signal area.

5. The intelligent positioning and marking method for anorectal lesion area according to claim 1, characterized in that: The method for obtaining the directional extension coefficient includes: A Cartesian coordinate system is established with the lower left corner of the perianal MRI image as the origin, and the image midline of the perianal MRI image perpendicular to the x-axis is selected as a reference straight line; Calculate the shortest distance between each pixel point in the texture curve in the target area and the reference straight line as the first distance; The directional extension coefficient is obtained according to the directional extension coefficient calculation formula, and the directional extension coefficient calculation formula is as follows: ； In the formula, Indicates the directional extension coefficient of the target area; Indicates the number of texture curves within the target area; Indicates the first The number of pixels in a texture curve; Indicates the first Texture curve The gradient of each pixel; Indicates the first Texture curve The gradient of each pixel; Indicates the first Texture curve The first distance of pixels.

6. The intelligent positioning and marking method for anorectal lesion area according to claim 1, characterized in that: The method for obtaining the anorectal lesion positioning coefficient includes: The anorectal lesion positioning coefficient is obtained according to the anorectal lesion positioning coefficient calculation formula, and the anorectal lesion positioning coefficient calculation formula is as follows: ； In the formula, Indicates the anorectal lesion localization coefficient of the target area; Indicates the directional extension coefficient of the target area; Indicates the number of pixels in the target area; Indicates the target area The distance between a pixel and the nearest pixel with the same gray value; Indicates the target area The number of other pixels in the preset neighborhood of a pixel; Indicates the target area The gray value of each pixel; Indicates the target area The preset neighborhood of pixels The grayscale value of other pixels.

7. The intelligent positioning and marking method for anorectal lesion area according to claim 1, characterized in that: The anorectal lesion area is located according to the anorectal lesion localization coefficient of each initial high signal area, including: Marking each initial high signal area in each of the perianal MRI images whose anorectal lesion localization coefficient is greater than a preset second threshold, and dividing the minimum circumscribed rectangle excluding the marked initial high signal area to obtain all suspected lesion areas in each perianal MRI image; All suspected lesion areas located at the same human body position in the perianal MRI images were overlapped, and the suspected lesion area with the most overlaps was taken as the anorectal lesion area.

8. An intelligent positioning and marking system for anorectal lesion areas, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for intelligent positioning and marking of anorectal lesion areas as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for intelligent positioning and marking of anorectal lesion areas as described in any one of claims 1 to 7 are implemented.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for intelligent positioning and marking of anorectal lesion areas as described in any one of claims 1 to 7 are implemented.

Images