Ultrasonic imaging omics analysis method and system for pathological characteristics of bladder tumor

Abstract

The invention relates to the technical field of image analysis, in particular to an ultrasonic imageomics analysis method and system for bladder tumor pathological characteristics, and the method comprises the steps: obtaining a target ultrasonic image corresponding to the bladder of a to-be-detected patient, and carrying out the connected domain division of the target ultrasonic image; determining a bladder cavity possible factor corresponding to the connected domain; determining an abnormal bulge index corresponding to each edge pixel point on the screened bladder cavity connected domain; screening out an abnormal convex edge from the bladder cavity communicating domain, and connecting two end points of the abnormal convex edge; according to the shape rule condition of the abnormal convex edge and the gray level distribution between the abnormal convex edge and the corresponding normal edge line segment, the bladder tumor characteristic index corresponding to the abnormal convex edge is determined, so that whether each abnormal convex edge is the bladder tumor edge or not can be judged. According to the invention, bladder tumor pathological feature analysis is carried out on the target ultrasonic image, bladder tumor identification is realized, and the accuracy of bladder tumor identification is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image analysis, and particularly to an ultrasonic imaging omics analysis method and system for pathological features of bladder tumors. Background Art

[0002] Ultrasonic imaging is a non-invasive method that uses the principle of high-frequency sound waves penetrating human tissues and reflecting back to generate two-dimensional images of internal tissues and organs in real time. Therefore, the application of ultrasonic imaging is relatively extensive. For example, it can be used to assist doctors in observing the situation of bladder tumors. However, due to the diversity of bladder tumors in terms of morphology, size, location, and growth pattern, it is often difficult for doctors to quickly identify bladder tumors during the observation process. Therefore, in order to facilitate doctors to observe the situation of bladder tumors, it is crucial to assist doctors in identifying bladder tumors based on ultrasonic images. Currently, when identifying an object, the commonly used method is to segment the object area from the object image through the Otsu threshold method.

[0003] However, when segmenting bladder tumors from ultrasonic images through the Otsu threshold method, the following technical problems often exist: Since the gray-scale difference between the bladder tumor area and the normal bladder area is often small, and the gray-scale of foreign objects such as stones and polyps in ultrasonic images is often similar to that of the bladder tumor area, when using the Otsu threshold method to segment bladder tumors based on the difference in gray-scale values, misjudgment of bladder tumor pixel points may occur, resulting in difficulty in accurately segmenting the bladder tumor area and poor accuracy in identifying bladder tumors. Summary of the Invention

[0004] To solve the technical problem of poor accuracy in identifying bladder tumors, the present invention proposes an ultrasonic imaging omics analysis method and system for pathological features of bladder tumors.

[0005] In a first aspect, the present invention provides an ultrasonic imaging omics analysis method for pathological features of bladder tumors, the method comprising: Obtaining a target ultrasonic image corresponding to the bladder of a patient to be detected, and performing connected component division on the target ultrasonic image; Determining a possible bladder cavity factor corresponding to each connected component according to the position of each connected component and the gray-scale distribution of each connected component, and screening out the bladder cavity connected components from all connected components based on the possible bladder cavity factor corresponding to the connected component; Determining an abnormal bulge index corresponding to each edge pixel point on the bladder cavity connected component according to the gray-scale difference between the inner and outer sides of each edge pixel point on the bladder cavity connected component and the edge tangent slope difference within a preset neighborhood edge segment corresponding to each edge pixel point on the bladder cavity connected component; Based on the abnormal bulge indicators corresponding to all the edge pixels of the bladder cavity connected region, filter out the abnormal bulge edges from the bladder cavity connected region, and connect the two endpoints of each abnormal bulge edge to obtain the normal edge segments corresponding to each abnormal bulge edge; According to the shape rule of each abnormal bulge edge and the gray-scale distribution between each abnormal bulge edge and its corresponding normal edge segment, determine the bladder tumor characteristic indicators corresponding to each abnormal bulge edge; According to the bladder tumor characteristic indicators corresponding to each abnormal bulge edge, determine whether each abnormal bulge edge is a bladder tumor edge.

[0006] Combined with the above first aspect, in a possible implementation manner, the determining the bladder cavity possible factor corresponding to each connected region according to the position of each connected region and the gray-scale distribution of each connected region includes: Determine any connected region in the target ultrasonic image as the marked connected region, and determine the center point of the target ultrasonic image as the reference center point; Determine the gray-scale representative value corresponding to the marked connected region as the gray-scale value with the largest number of pixels in the marked connected region; According to the distance between the center point of the marked connected region and the reference center point, the gray-scale representative value corresponding to the marked connected region, and the number of pixels in the marked connected region whose corresponding gray-scale value is equal to its corresponding gray-scale representative value, determine the bladder cavity possible factor corresponding to the marked connected region, where the center point of the marked connected region is used to represent the position of the marked connected region.

[0007] Combined with the above first aspect, in a possible implementation manner, the screening out the bladder cavity connected region from all connected regions based on the bladder cavity possible factor corresponding to the connected region includes: Screen out the connected region with the largest corresponding bladder cavity possible factor from all connected regions as the bladder cavity connected region.

[0008] Combined with the above first aspect, in a possible implementation manner, the formula for the bladder cavity possible factor corresponding to the marked connected region is: ; where, is the bladder cavity possible factor corresponding to the marked connected region; is the number of pixels in the marked connected region whose corresponding gray-scale value is equal to its corresponding gray-scale representative value; is the exponential function with the natural constant as the base; is the gray-scale representative value corresponding to the marked connected region; is the distance between the center point of the marked connected region and the reference center point.

[0009] Combined with the above first aspect, in a possible implementation, determining the abnormal bulge index corresponding to each edge pixel point on the bladder cavity connected domain according to the gray-scale difference between the inner and outer sides of each edge pixel point on the bladder cavity connected domain and the edge tangent slope difference within the preset neighborhood edge segment corresponding to each edge pixel point on the bladder cavity connected domain includes: Determine an arbitrary edge pixel point on the bladder cavity connected domain as a marked edge point, and filter out a preset number of pixel points closest to the marked edge point on both sides of the marked edge point along the normal line of the marked edge point to form two pixel point sequences corresponding to the marked edge point; Determine the gray-scale difference between the inner and outer sides corresponding to the marked edge point according to the gray-scale difference between the two pixel point sequences corresponding to the marked edge point; Determine the edge tangent slope difference corresponding to the marked edge point according to the difference between the slopes of the tangents corresponding to adjacent edge pixel points within the preset neighborhood edge segment corresponding to the marked edge point; Determine the abnormal bulge index corresponding to the marked edge point according to the gray-scale difference between the inner and outer sides and the edge tangent slope difference corresponding to the marked edge point.

[0010] Combined with the above first aspect, in a possible implementation, the formula corresponding to the abnormal bulge index of the marked edge point is: ; ; ; wherein, is the abnormal bulge index corresponding to the marked edge point; is the absolute value function; is the gray-scale difference between the inner and outer sides corresponding to the marked edge point; is the mean value of the gray-scale differences between the inner and outer sides corresponding to all edge pixel points on the bladder cavity connected domain; is the edge tangent slope difference corresponding to the marked edge point; is the mean value of the edge tangent slope differences corresponding to all edge pixel points on the bladder cavity connected domain; is the mean value of the gray-scale values corresponding to all pixel points in any one of the pixel point sequences corresponding to the marked edge point; is the mean value of the gray-scale values corresponding to all pixel points in the other pixel point sequence corresponding to the marked edge point; is the number of edge pixel points within the preset neighborhood edge segment; is the serial number of the edge pixel point within the preset neighborhood edge segment; is the slope of the tangent corresponding to the th edge pixel point within the preset neighborhood edge segment corresponding to the marked edge point; is the slope corresponding to the tangent line of the th edge pixel point within the preset neighborhood edge segment corresponding to the marked edge point.

[0011] Combined with the above first aspect, in a possible implementation, screening out the abnormal protrusion edges from the bladder cavity connected domain based on the abnormal protrusion indexes corresponding to all edge pixel points on the bladder cavity connected domain includes: If the abnormal protrusion index corresponding to the edge pixel point is greater than the preset abnormal threshold, then determine the edge pixel point as an abnormal edge point; Form the abnormal protrusion edges with consecutive abnormal edge points.

[0012] Combined with the above first aspect, in a possible implementation, determining the bladder tumor characteristic index corresponding to each abnormal protrusion edge according to the shape rule condition of each abnormal protrusion edge and the gray level distribution between each abnormal protrusion edge and its corresponding normal edge line segment includes: Determine any one of the abnormal protrusion edges as the marked protrusion edge, and obtain the edge chain code sequence corresponding to the marked protrusion edge, where the edge chain code sequence corresponding to the marked protrusion edge is used to characterize the shape rule condition of the marked protrusion edge; Determine the normal line of the center point of the marked protrusion edge as the target normal line corresponding to the marked protrusion edge, and determine the intersection point between the target normal line and the normal edge line segment corresponding to the marked protrusion edge as the target intersection point corresponding to the marked protrusion edge; Connect the center point of the marked protrusion edge and the target intersection point to obtain the target line segment corresponding to the marked protrusion edge, where the starting point of the target line segment is the center point of the marked protrusion edge, and the ending point of the target line segment is the target intersection point; Determine the bladder tumor characteristic index corresponding to the marked protrusion edge according to the edge chain code sequence corresponding to the marked protrusion edge and the gray level distribution on the target line segment corresponding to the marked protrusion edge.

[0013] Combined with the above first aspect, in a possible implementation, the formula corresponding to the bladder tumor characteristic index of the marked protrusion edge is: ; where is the bladder tumor characteristic index corresponding to the marked protrusion edge; is the normalization function; is the mean value of all chain codes in the edge chain code sequence corresponding to the marked protrusion edge; is the number of pixel points on the target line segment corresponding to the marked protrusion edge; is the serial number of the pixel points on the target line segment corresponding to the marked raised edge; is the gray value corresponding to the th pixel point on the target line segment corresponding to the marked raised edge; gray value corresponding to the th pixel point on the target line segment corresponding to the marked raised edge;

[0014] In a second aspect, the present invention provides an ultrasound imaging omics analysis system for bladder tumor pathological features, and the system includes: An acquisition and division module, configured to acquire a target ultrasound image corresponding to the bladder of a patient to be detected, and perform connected component division on the target ultrasound image; A determination and screening module, configured to determine a possible bladder cavity factor corresponding to each connected component according to the position of each connected component and the gray distribution of each connected component, and screen out the bladder cavity connected components from all connected components based on the possible bladder cavity factor corresponding to the connected component; An abnormal protrusion index determination module, configured to determine an abnormal protrusion index corresponding to each edge pixel point on the bladder cavity connected component according to the gray difference between the inner and outer sides of each edge pixel point on the bladder cavity connected component and the edge tangent slope difference within a preset neighborhood edge segment corresponding to each edge pixel point on the bladder cavity connected component; A screening and connection module, configured to screen out abnormal protrusion edges from the bladder cavity connected component based on the abnormal protrusion indexes corresponding to all edge pixel points on the bladder cavity connected component, and connect the two endpoints of each abnormal protrusion edge to obtain a normal edge line segment corresponding to each abnormal protrusion edge; A bladder tumor feature index determination module, configured to determine a bladder tumor feature index corresponding to each abnormal protrusion edge according to the shape regularity of each abnormal protrusion edge and the gray distribution between each abnormal protrusion edge and its corresponding normal edge line segment; A bladder tumor edge judgment module, configured to judge whether each abnormal protrusion edge is a bladder tumor edge according to the bladder tumor feature index corresponding to each abnormal protrusion edge.

[0015] In a third aspect, a server is provided, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the method in the first aspect or any possible implementation manner of the first aspect.

[0016] In a fourth aspect, a computer program product is provided, and the computer program product includes: computer program code, when the computer program code runs on a computer, enabling the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0017] In a fifth aspect, a computer-readable storage medium is provided, which stores computer program code. When the computer program code runs on a computer, the computer is caused to execute the method in the above first aspect or any possible implementation manner of the first aspect.

[0018] The present invention has the following beneficial effects: An ultrasonic imaging genomics analysis method for pathological features of bladder tumors according to the present invention realizes the identification of bladder tumors by analyzing the pathological features of bladder tumors in target ultrasonic images, solves the technical problem of poor accuracy in identifying bladder tumors, and improves the accuracy of identifying bladder tumors. Compared with using the Otsu threshold method to segment bladder tumors based on different gray values, the present invention comprehensively considers multiple factors related to the characteristics of bladder tumors, such as gray difference, edge tangent slope difference, abnormal bulge index, shape regularity, and bladder tumor feature index, etc., thereby improving the accuracy of identifying bladder tumors and better assisting doctors in observing bladder tumors. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for describing the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a flowchart of an ultrasonic imaging genomics analysis method for pathological features of bladder tumors according to the present invention; Figure 2 It is a schematic diagram of the composition structure of an ultrasonic imaging genomics analysis system for pathological features of bladder tumors according to the present invention; Figure 3 It is a schematic diagram of the structure of a computer device according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0021] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will describe in detail the specific implementation manners, structures, features, and effects of the technical solutions proposed according to the present invention in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this invention belongs.

[0023] Reference Figure 1 , which shows the flow of some embodiments of an ultrasonic imaging genomics analysis method for the pathological characteristics of bladder tumors of the present invention. The ultrasonic imaging genomics analysis method for the pathological characteristics of bladder tumors includes the following steps: Step S1, obtaining a target ultrasonic image corresponding to the bladder of the patient to be detected, and performing connected component segmentation on the target ultrasonic image.

[0024] Among them, the patient to be detected can be a patient to be subjected to bladder tumor detection. The target ultrasonic image can be the ultrasonic image of the bladder of the patient to be detected after image preprocessing. Image preprocessing can include, but is not limited to: denoising processing and image enhancement.

[0025] It should be noted that when the patient's bladder is in a full state, a high-frequency ultrasonic device is used to scan the patient's bladder to obtain a more detailed ultrasonic image of the patient's bladder. The normal bladder wall usually appears as a bright echo band in the ultrasonic image, and when the bladder is full, the urine in the cavity is often anechoic; however, the bladder tumor usually appears as an abnormal echo mass protruding into the bladder cavity in the ultrasonic image. The ultrasonic device performs ultrasonic imaging on the patient's bladder based on the echo performance. Performing image preprocessing on the image data can improve the image quality.

[0026] As an example, this step can include the following steps: The first step, through the ultrasonic device, collecting the ultrasonic image of the bladder of the patient to be detected, performing denoising processing on the ultrasonic image, performing image enhancement on the denoised ultrasonic image, and taking the ultrasonic image after image enhancement at this time as the target ultrasonic image.

[0027] The second step, through the connected component extraction algorithm, performing connected component extraction on the target ultrasonic image to obtain a plurality of connected components, realizing the connected component segmentation.

[0028] Step S2, determining the possible bladder cavity factor corresponding to each connected component according to the position of each connected component and the gray level distribution of each connected component, and screening out the bladder cavity connected components from all the connected components based on the possible bladder cavity factor corresponding to the connected component.

[0029] Among them, the bladder cavity connected component can be used to represent the bladder cavity.

[0030] It should be noted that within the bladder wall of a patient, tumors often grow inside the bladder cavity and adhere closely to the bladder wall. In ultrasonic images, this is manifested as a structure protruding into the bladder cavity, causing local thickening of the bladder wall and possibly abnormal connections with surrounding tissues. Therefore, identifying the bladder cavity where bladder tumors may exist can, to a certain extent, reduce the interference of irrelevant information, making tumor characteristics more prominent, and thus facilitating the subsequent accurate identification of bladder tumors.

[0031] As an example, this step may include the following steps: First step, determine any connected component in the above-mentioned target ultrasonic image as the marked connected component, and determine the center point of the above-mentioned target ultrasonic image as the reference center point.

[0032] Second step, determine the gray value with the largest number of pixels in the above-mentioned marked connected component as the gray representative value corresponding to the above-mentioned marked connected component.

[0033] Third step, determine the possible factor of the bladder cavity corresponding to the above-mentioned marked connected component according to the distance between the center point of the above-mentioned marked connected component and the reference center point, the gray representative value corresponding to the above-mentioned marked connected component, and the number of pixels in the above-mentioned marked connected component whose corresponding gray value is equal to its corresponding gray representative value.

[0034] Among them, the center point of the marked connected component can be used to represent the position of the marked connected component.

[0035] It should be noted that in ultrasonic images, the bladder usually appears as a liquid anechoic area, which is often surrounded by a highly echogenic bladder wall. Bladder tumors usually grow attached to the inner wall of the bladder and grow towards the cavity, appearing as a protrusion in ultrasonic images. The bladder cavity often presents as a connected component with a relatively dark color and a relatively large area. Secondly, the human bladder usually has a fixed and similar shape. The tissue outside the cavity and the part of the bladder tumor often present the characteristic of a light color. First, perform connected component segmentation on the ultrasonic image of the patient's bladder. In the ultrasonic image, the liquid in the bladder cavity, the tissue outside the cavity, and the foreign objects in the cavity should be divided into different connected components in order to segment the target abnormal area and facilitate the doctor's diagnosis. However, it is found in actual observation that the result of the connected component segmentation cannot fully achieve accurate segmentation. When the patient has a bladder tumor, it may be connected to the tissue outside the cavity as the same connected component. That is to say, the connected component of the bladder cavity divided often includes the bladder cavity and the foreign objects in the bladder cavity. The foreign objects in the bladder cavity can include: bladder tumors, stones, polyps, etc. Therefore, the subsequent embodiments of the present invention mainly analyze the connected component of the bladder cavity to adaptively segment the bladder tumor area.

[0036] For example, the formula for determining the possible factor of the bladder cavity corresponding to the marked connected component can be: ; Among them, is the possible factor of the bladder cavity corresponding to the labeled connected component. is the number of pixel points in the labeled connected component whose corresponding gray value is equal to its corresponding gray representative value, that is, the number of pixel points in the labeled connected component whose corresponding gray value is equal to . is the exponential function with the natural constant as the base. is the gray representative value corresponding to the labeled connected component. is the distance between the center point of the labeled connected component and the reference center point.

[0037] It should be noted that in actual situations, the bladder cavity often appears as a connected component with a relatively dark color and a relatively large area. Therefore, the gray value of the pixel points in the bladder cavity is often close to zero, and when the doctor moves the ultrasound probe on the patient's body surface, the target is the bladder area. Therefore, the bladder is always close to the center position of the ultrasound image. When is larger, it often indicates that there are more pixel points in the labeled connected component whose corresponding gray value is equal to . When is smaller, it often indicates that the overall gray value of the labeled connected component is smaller. When is smaller, it often indicates that the center point of the labeled connected component is closer to the center position of the ultrasound image. Therefore, when is larger, it often indicates that the overall color of the labeled connected component is darker and the area of its darker color region is larger, and it is closer to the center position of the ultrasound image; it often indicates that the labeled connected component is more likely to represent the bladder cavity.

[0038] Step 4: Select the connected component with the largest possible factor of the bladder cavity from all connected components as the connected component of the bladder cavity.

[0039] Step S3: Determine the abnormal bulge index corresponding to each edge pixel point on the connected component of the bladder cavity according to the gray value difference between the inner and outer sides of each edge pixel point on the connected component of the bladder cavity and the edge tangent slope difference within the preset neighborhood edge segment corresponding to each edge pixel point on the connected component of the bladder cavity.

[0040] Among them, the preset neighborhood edge segment corresponding to the edge pixel point can be a sub-edge intercepted from the edge of the connected component of the bladder cavity and located around the edge pixel point. The edge pixel point can be located at the center of its corresponding preset neighborhood edge segment. The number of edge pixel points in the preset neighborhood edge segment can be a preset number. For example, the number of edge pixel points in the preset neighborhood edge segment can be 15. The number of edge pixel points in the preset neighborhood edge segment can also be equal to the average length of the bladder tumors detected historically.

[0041] It should be noted that bladder tumors usually grow inside the bladder cavity and adhere closely to the bladder wall. In ultrasound images, they appear as structures protruding into the bladder cavity, causing local thickening of the bladder wall and possibly abnormal connections with surrounding tissues. The bladder wall at the location of the bladder tumor often shows significant bulging changes. Such morphological changes result in a greater difference in the tangent slope of the pixel points at the tumor edge compared to the surrounding healthy tissues. That is to say, the change in the tangent slope of the pixel points at the tumor edge is often more drastic, with a relatively large difference from the overall performance. In addition, since bladder tumors are diseased tissues, there are often certain differences in the color distribution shown in ultrasound images compared to the healthy bladder wall. That is to say, when the gray-scale change of the pixel points on both sides of the bladder wall is abnormal, it often indicates that there may be a bladder tumor or other foreign objects at that location of the bladder wall. Therefore, the larger the abnormal bulging index corresponding to the quantified edge pixel points, the more likely it is that the edge pixel points are the edge pixel points of the bladder tumor.

[0042] As an example, this step may include the following steps: First step, determine any edge pixel point on the connected domain of the bladder cavity as the marked edge point. From the normal line of the above-mentioned marked edge point and on both sides of the marked edge point, screen out the preset number of pixel points closest to the marked edge point respectively to form two pixel point sequences corresponding to the above-mentioned marked edge point.

[0043] Among them, the preset number can be the number of pixel points set in advance. For example, the preset number can be 10. A pixel point sequence can be composed of the preset number of pixel points on one side of the marked edge point.

[0044] Second step, determine the gray-scale difference between the inside and outside of the above-mentioned marked edge point according to the gray-scale difference between the two pixel point sequences corresponding to the above-mentioned marked edge point.

[0045] Third step, determine the edge tangent slope difference corresponding to the above-mentioned marked edge point according to the difference between the slopes of the tangents corresponding to the adjacent edge pixel points within the preset neighborhood edge segment corresponding to the above-mentioned marked edge point.

[0046] Fourth step, determine the abnormal bulging index corresponding to the above-mentioned marked edge point according to the gray-scale difference between the inside and outside and the edge tangent slope difference corresponding to the above-mentioned marked edge point.

[0047] For example, the formula for determining the abnormal bulging index corresponding to the marked edge point can be: ; ; ; Among them, is the abnormal bulging index corresponding to the marked edge point. is the absolute value function. is to mark the gray-scale difference between the inner and outer sides corresponding to the edge points. is the average value of the gray-scale differences between the inner and outer sides corresponding to all the edge pixels on the connected region of the bladder cavity. is to mark the difference in the slope of the edge tangent corresponding to the edge points. is the average value of the differences in the slope of the edge tangent corresponding to all the edge pixels on the connected region of the bladder cavity. is the average value of the gray-scale values corresponding to all the pixels in any pixel point sequence corresponding to the marked edge point. is the average value of the gray-scale values corresponding to all the pixels in another pixel point sequence corresponding to the marked edge point. is the number of edge pixels within the preset neighborhood edge segment. is the serial number of the edge pixels within the preset neighborhood edge segment. is within the preset neighborhood edge segment corresponding to the marked edge point, the slope corresponding to the tangent of the th edge pixel. is the slope corresponding to the tangent of the

[0048] It should be noted that can characterize the gray-scale change of the pixels on both sides of the bladder wall where the marked edge point is located. can characterize the overall gray-scale change of the pixels on both sides of the bladder wall. Therefore, when is larger, it often indicates that the difference between the gray-scale change of the pixels on both sides of the bladder wall where the marked edge point is located and the overall gray-scale change of the pixels on both sides of the bladder wall is larger; it often indicates that the marked edge point is more likely to be an abnormal convex edge pixel point, and it often indicates that the marked edge point is more likely to be a tumor edge pixel point. can characterize the change situation of the edge slope around the marked edge point. can characterize the overall change situation of the edge slope of the bladder wall. Therefore, when is larger, it often indicates that the difference between the degree of change of the edge slope around the marked edge point and the overall degree of change of the edge slope of the bladder wall is larger; it often indicates that the marked edge point is more likely to be an abnormal convex edge pixel point, and it often indicates that the marked edge point is more likely to be a tumor edge pixel point. Therefore, when is larger, it often indicates that the marked edge point is more likely to be an abnormal convex edge pixel point, and it often indicates that the marked edge point is more likely to be a tumor edge pixel point.

[0049] Step S4: Based on the abnormal bulge indicators corresponding to all the edge pixels of the bladder cavity connected region, screen out the abnormal bulge edges from the bladder cavity connected region, and connect the two endpoints of each abnormal bulge edge to obtain the normal edge segments corresponding to each abnormal bulge edge.

[0050] Among them, the closed area formed by an abnormal bulge edge and its corresponding normal edge segment can represent a foreign object. The abnormal bulge edge can be the edge bulging on the bladder wall. The normal edge segment can represent the bladder wall edge blocked by the foreign object.

[0051] As an example, this step may include the following steps: The first step: If the abnormal bulge indicator corresponding to the edge pixel is greater than the preset abnormal threshold, then determine the edge pixel as an abnormal edge point.

[0052] Among them, the preset abnormal threshold can be a threshold set in advance for judging whether a pixel is abnormal. For example, the preset abnormal threshold can be 0.6.

[0053] The second step: Form the abnormal bulge edges by connecting the continuous abnormal edge points.

[0054] The third step: Connect the two endpoints of each abnormal bulge edge to obtain the normal edge segments corresponding to each abnormal bulge edge.

[0055] Step S5: According to the shape rule condition of each abnormal bulge edge and the gray-scale distribution between each abnormal bulge edge and its corresponding normal edge segment, determine the bladder tumor characteristic index corresponding to each abnormal bulge edge.

[0056] It should be noted that in addition to bladder tumors, there may also be foreign objects such as stones and polyps in the bladder cavity. These foreign objects may form strong echo masses or medium echo areas on the ultrasound image, and their positions in the bladder cavity are not fixed. When they are close to the bladder wall, they often interfere with the recognition of the changes in the bladder wall edge caused by bladder tumors. Therefore, for the connected regions corresponding to all the bulge edges, that is, the regions formed by the abnormal bulge edges and their corresponding normal edge segments, when performing bladder tumor recognition on these regions, bladder tumor characteristic analysis is often required. Generally speaking, the edges of bladder tumors are often irregular, and when collecting ultrasound images, the patient's bladder is often in a filled state. In this state, due to its growth characteristics, the bladder tumor will infiltrate into the bladder wall to a greater extent, and this infiltration is manifested as a gradually decreasing trend in the gray-scale value between the bulge edge of the bladder tumor and its corresponding bladder wall on the ultrasound image.

[0057] As an example, this step may include the following steps: First step, determine any abnormal convex edge as the marked convex edge, and obtain the edge chain code sequence corresponding to the above-mentioned marked convex edge.

[0058] Among them, the edge chain code sequence corresponding to the above-mentioned marked convex edge can be used to characterize the shape regularity of the above-mentioned marked convex edge. The edge chain code sequence can be obtained through the Freeman chain code algorithm.

[0059] Second step, determine the normal line of the center point of the above-mentioned marked convex edge as the target normal line corresponding to the above-mentioned marked convex edge, and determine the intersection point between the above-mentioned target normal line and the normal edge segment corresponding to the above-mentioned marked convex edge as the target intersection point corresponding to the above-mentioned marked convex edge.

[0060] Third step, connect the center point of the above-mentioned marked convex edge and the above-mentioned target intersection point to obtain the target line segment corresponding to the above-mentioned marked convex edge.

[0061] Among them, the starting point of the target line segment can be the center point of the above-mentioned marked convex edge, and the ending point of the target line segment can be the above-mentioned target intersection point.

[0062] Fourth step, determine the bladder tumor characteristic index corresponding to the above-mentioned marked convex edge according to the edge chain code sequence corresponding to the above-mentioned marked convex edge and the gray-scale distribution on the target line segment corresponding to the above-mentioned marked convex edge.

[0063] For example, the formula for determining the bladder tumor characteristic index corresponding to the marked convex edge can be: ; Among them, is the bladder tumor characteristic index corresponding to the marked convex edge. is the normalization function. is the mean value of all chain codes in the edge chain code sequence corresponding to the marked convex edge. is the number of pixel points on the target line segment corresponding to the marked convex edge. is the serial number of the pixel point on the target line segment corresponding to the marked convex edge. is the th pixel point on the target line segment corresponding to the marked convex edge. is the th pixel point on the target line segment corresponding to the marked convex edge. is a preset factor greater than 0, mainly used to prevent the denominator from being 0. For example, can be 0.001.

[0064] It should be noted that when The larger it is, it often indicates that the chain code in the edge chain code sequence corresponding to the marked convex edge is larger, which often indicates that the marked convex edge is more irregular, and often indicates that the marked convex edge is more likely to be the edge of a bladder tumor. When The larger it is, it often indicates that the gray values of the pixel points corresponding to the target line segment corresponding to the marked convex edge show a decreasing trend. Therefore, when The larger it is, it often indicates that the degree of decrease in the gray values of the pixel points corresponding to the target line segment corresponding to the marked convex edge is greater, which often indicates that the gray values between the marked convex edge and its corresponding normal edge line segment show a gradually decreasing situation, and often indicates that the marked convex edge is more likely to be the edge of a bladder tumor. Therefore, when The larger it is, it often indicates that the marked convex edge is more likely to be the edge of a bladder tumor.

[0065] Step S6: According to the bladder tumor characteristic indexes corresponding to each abnormal convex edge, determine whether each abnormal convex edge is the edge of a bladder tumor.

[0066] As an example, if the bladder tumor characteristic index corresponding to the abnormal convex edge is greater than the preset tumor characteristic threshold, then the abnormal convex edge is determined to be the edge of a bladder tumor. Among them, the preset tumor characteristic threshold can be a threshold set in advance for tumor judgment. For example, the preset tumor characteristic threshold can be 0.8.

[0067] Optionally, for the convenience of doctors' observation, different colors can be used to mark the abnormal convex edges corresponding to different bladder tumor characteristic indexes and their corresponding normal edge line segments, making the pathological characteristics of the patient clearer and more intuitive, and providing auxiliary diagnosis information on the pathological characteristics of bladder tumors for doctors. This method not only enhances the readability of the image, but also enables doctors to quickly capture key information, and this information helps doctors formulate personalized treatment plans, improve the treatment effect and the quality of life of patients.

[0068] It should be noted that due to the diversity of bladder tumors in terms of morphology, size, location and growth pattern, doctors face huge challenges in the diagnosis process. Therefore, accurately identifying tumors and their boundaries in ultrasound images is crucial. The subtle feature manifestations of tumors and the detailed delineation of tumor boundaries in ultrasound images are often the premise for revealing key information such as the morphology, size, location of tumors and their relationship with surrounding tissues. These information are important references for doctors to evaluate the pathological characteristics of tumors, formulate treatment plans and predict the prognosis of patients. Therefore, achieving accurate identification and boundary delineation of bladder tumors in ultrasound images is the key to improving the diagnostic accuracy and treatment effect of bladder tumors.

[0069] Reference Figure 2, based on the same inventive concept as the above method embodiments, the present invention provides an ultrasound imaging omics analysis system for bladder tumor pathological features. The system includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of an ultrasound imaging omics analysis method for bladder tumor pathological features, which may specifically include: An acquisition and division module 201, configured to acquire a target ultrasound image corresponding to the bladder of a patient to be detected, and perform connected component division on the target ultrasound image; A determination and screening module 202, configured to determine a possible bladder cavity factor corresponding to each connected component according to the position of each connected component and the gray-scale distribution of each connected component, and screen out the connected components of the bladder cavity from all connected components based on the possible bladder cavity factor corresponding to the connected component; An abnormal protrusion index determination module 203, configured to determine an abnormal protrusion index corresponding to each edge pixel point on the connected component of the bladder cavity according to the gray-scale difference between the inner and outer sides of each edge pixel point on the connected component of the bladder cavity and the edge tangent slope difference within a preset neighborhood edge segment corresponding to each edge pixel point on the connected component of the bladder cavity; A screening and connection module 204, configured to screen out abnormal protrusion edges from the connected component of the bladder cavity based on the abnormal protrusion indexes corresponding to all edge pixel points on the connected component of the bladder cavity, and connect the two end points of each abnormal protrusion edge to obtain a normal edge line segment corresponding to each abnormal protrusion edge; A bladder tumor feature index determination module 205, configured to determine a bladder tumor feature index corresponding to each abnormal protrusion edge according to the shape regularity of each abnormal protrusion edge and the gray-scale distribution between each abnormal protrusion edge and its corresponding normal edge line segment; A bladder tumor edge judgment module 206, configured to judge whether each abnormal protrusion edge is a bladder tumor edge according to the bladder tumor feature index corresponding to each abnormal protrusion edge.

[0070] Figure 3 It is a schematic structural diagram of a computer device provided by an embodiment of the present invention. Exemplarily, as Figure 3 shown, the computer device 300 includes: a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302. When the processor 302 executes the computer program 303, the computer device can execute any of the above-introduced ultrasound imaging omics analysis methods for bladder tumor pathological features.

[0071] Based on the same inventive concept as the above method embodiments, the present invention provides a server, comprising a memory and a processor. The memory is used for storing executable program codes, and the processor is used for calling and running the executable program codes from the memory, so that the device executes any one of the above ultrasonic imaging omics analysis methods for bladder tumor pathological features.

[0072] Based on the same inventive concept as the above method embodiments, the present invention provides a computer program product, which comprises: computer program codes, when the computer program codes are run on a computer, enabling the computer to execute any one of the above ultrasonic imaging omics analysis methods for bladder tumor pathological features.

[0073] Based on the same inventive concept as the above method embodiments, the present invention provides a computer-readable storage medium, which stores computer program codes, when the computer program codes are run on a computer, enabling the computer to execute any one of the above ultrasonic imaging omics analysis methods for bladder tumor pathological features.

[0074] In summary, compared with using the Otsu threshold method to segment bladder tumors by using different gray values, the present invention comprehensively considers multiple factors related to bladder tumor features, such as gray difference, edge tangent slope difference, abnormal bulge index, shape regularity, and bladder tumor feature index, etc., thereby improving the accuracy of bladder tumor recognition and better assisting doctors in observing bladder tumors.

[0075] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and all should be included in the protection scope of the present invention.

Claims

1. A method for analyzing the pathological characteristics of bladder tumors using ultrasound imaging genomics, characterized in that: The following steps are involved: Acquire a target ultrasound image corresponding to the bladder of the patient to be tested, and divide the target ultrasound image into connected domains; According to the position of each connected domain and the grayscale distribution of each connected domain, the possible factors of the bladder cavity corresponding to each connected domain are determined, and based on the possible factors of the bladder cavity corresponding to the connected domain, the bladder cavity connected domain is screened out from all connected domains; According to the grayscale difference between the inner and outer sides of each edge pixel point on the bladder cavity connected domain and the edge tangent slope difference within the preset neighborhood edge segment corresponding to each edge pixel point on the bladder cavity connected domain, the abnormal protrusion index corresponding to each edge pixel point on the bladder cavity connected domain is determined; Based on the abnormal convexity indexes corresponding to all edge pixels on the bladder cavity connected domain, the abnormal convexity edges are screened out from the bladder cavity connected domain, and the two endpoints of each abnormal convexity edge are connected to obtain the normal edge segment corresponding to each abnormal convexity edge; According to the shape regularity of each abnormal raised edge and the grayscale distribution between each abnormal raised edge and its corresponding normal edge line segment, the bladder tumor characteristic index corresponding to each abnormal raised edge is determined; According to the bladder tumor characteristic index corresponding to each abnormal raised edge, it is determined whether each abnormal raised edge is a bladder tumor edge.

2. The method for analyzing the pathological characteristics of bladder tumors by ultrasound imaging genomics according to claim 1, characterized in that: Determining the possible factors of the bladder cavity corresponding to each connected domain according to the position of each connected domain and the grayscale distribution of each connected domain includes: Determine any connected domain in the target ultrasound image as a marked connected domain, and determine the center point of the target ultrasound image as a reference center point; Determine the grayscale value with the most pixels in the marked connected domain as the grayscale representative value corresponding to the marked connected domain; The possible factor of the bladder cavity corresponding to the marked connected domain is determined according to the distance between the center point of the marked connected domain and the reference center point, the grayscale representative value corresponding to the marked connected domain, and the number of pixel points in the marked connected domain whose corresponding grayscale values ​​are equal to their corresponding grayscale representative values, wherein the center point of the marked connected domain is used to characterize the position of the marked connected domain.

3. The method for analyzing the pathological characteristics of bladder tumors by ultrasound imaging genomics according to claim 1, characterized in that: The method of screening out the bladder cavity connected domain from all connected domains based on the possible factors of the bladder cavity corresponding to the connected domains includes: The connected domain with the largest possible factor of the corresponding bladder cavity is selected from all connected domains as the bladder cavity connected domain.

4. The method for analyzing the pathological characteristics of bladder tumors by ultrasound imaging genomics according to claim 2, characterized in that: The formula corresponding to the possible factors of the bladder cavity corresponding to the marked connected domain is: ； in, is the possible factor of the bladder cavity corresponding to the marked connected domain; It is the number of pixels whose corresponding grayscale value in the marked connected domain is equal to its corresponding grayscale representative value; It is an exponential function with a natural constant as base; is the grayscale representative value corresponding to the marked connected domain; is the distance between the center point of the marked connected domain and the reference center point.

5. The method for analyzing the pathological characteristics of bladder tumors by ultrasound imaging genomics according to claim 1, characterized in that: Determining the abnormal protrusion index corresponding to each edge pixel point on the bladder cavity connected domain according to the grayscale difference between the inner and outer sides of each edge pixel point on the bladder cavity connected domain and the edge tangent slope difference in the preset neighborhood edge segment corresponding to each edge pixel point on the bladder cavity connected domain includes: Determine any edge pixel point on the connected domain of the bladder cavity as a marked edge point, and select a preset number of pixel points closest to the marked edge point on the normal line of the marked edge point and on both sides of the marked edge point to form two pixel point sequences corresponding to the marked edge point; Determine the grayscale difference between the inner and outer sides corresponding to the marked edge point according to the grayscale difference between the two pixel point sequences corresponding to the marked edge point; Determine the edge tangent slope difference corresponding to the marked edge point according to the difference between the slopes corresponding to the tangents of adjacent edge pixel points in the preset neighborhood edge segment corresponding to the marked edge point; According to the grayscale difference between the inner and outer sides and the edge tangent slope difference corresponding to the marked edge point, the abnormal protrusion index corresponding to the marked edge point is determined.

6. The method for analyzing the pathological characteristics of bladder tumors by ultrasound imaging genomics according to claim 5, characterized in that: The formula corresponding to the abnormal convexity index corresponding to the marked edge point is: ； ； ； in, is the abnormal convexity indicator corresponding to the marked edge point; It is the absolute value function; It is the grayscale difference between the inner and outer sides corresponding to the marked edge point; It is the mean value of the grayscale difference between the inner and outer sides corresponding to all edge pixels in the connected domain of the bladder cavity; is the difference in the slope of the edge tangent corresponding to the marked edge point; It is the mean value of the edge tangent slope differences corresponding to all edge pixels in the connected domain of the bladder cavity; It is the mean of the grayscale values ​​corresponding to all pixels in any pixel sequence corresponding to the marked edge point; It is the mean of the grayscale values ​​corresponding to all pixels in another pixel sequence corresponding to the marked edge point; is the number of edge pixels in the preset neighborhood edge segment; is the serial number of the edge pixel point in the preset neighborhood edge segment; It is the preset neighborhood edge segment corresponding to the marked edge point. The slope of the tangent line corresponding to the edge pixel point; It is the preset neighborhood edge segment corresponding to the marked edge point. The slope of the tangent line corresponding to the edge pixel point.

7. The method for analyzing the pathological characteristics of bladder tumors by ultrasound imaging genomics according to claim 1, characterized in that: The abnormal convexity index corresponding to all edge pixels on the bladder cavity connected domain is used to screen out abnormal convexity edges from the bladder cavity connected domain, including: If the abnormal protrusion index corresponding to the edge pixel point is greater than the preset abnormal threshold, the edge pixel point is determined as an abnormal edge point; Continuous abnormal edge points are combined to form abnormal raised edges.

8. The method for analyzing the pathological characteristics of bladder tumors by ultrasound imaging genomics according to claim 1, characterized in that: Determining the bladder tumor characteristic index corresponding to each abnormal convex edge according to the shape regularity of each abnormal convex edge and the grayscale distribution between each abnormal convex edge and its corresponding normal edge line segment includes: Determine any abnormal raised edge as a marked raised edge, and obtain an edge chain code sequence corresponding to the marked raised edge, wherein the edge chain code sequence corresponding to the marked raised edge is used to characterize the shape regularity of the marked raised edge; Determine the normal of the center point of the marked raised edge as the target normal corresponding to the marked raised edge, and determine the intersection point between the target normal and the normal edge line segment corresponding to the marked raised edge as the target intersection point corresponding to the marked raised edge; Connecting the center point of the raised edge of the mark and the target intersection point to obtain a target line segment corresponding to the raised edge of the mark, wherein the starting point of the target line segment is the center point of the raised edge of the mark, and the ending point of the target line segment is the target intersection point; The bladder tumor characteristic index corresponding to the marked raised edge is determined according to the edge chain code sequence corresponding to the marked raised edge and the grayscale distribution on the target line segment corresponding to the marked raised edge.

9. The method for analyzing the pathological characteristics of bladder tumors by ultrasound imaging genomics according to claim 8, characterized in that: The formula corresponding to the bladder tumor characteristic index corresponding to the marked raised edge is: ； in, It is a characteristic indicator of bladder tumor corresponding to the raised edge of the mark; is the normalization function; is the mean of all chain codes in the edge chain code sequence corresponding to the marked raised edge; is the number of pixels on the target line segment corresponding to the marked raised edge; is the serial number of the pixel point on the target line segment corresponding to the marked raised edge; It is the first The gray value corresponding to each pixel; It is the first The gray value corresponding to each pixel; is a pre-set factor greater than 0.

10. An ultrasound imaging genomics analysis system for bladder tumor pathological characteristics, characterized in that: The method comprises a processor and a memory, wherein the processor is used to process instructions stored in the memory to implement an ultrasound imaging genomics analysis method for pathological characteristics of bladder tumors according to any one of claims 1 to 9.

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