Liver and gall CT image processing method and system

Abstract

The invention relates to a liver and gall CT image processing method and system, and relates to the technical field of image processing. According to the liver and gall CT image processing method, a liver and gall CT image is obtained, a liver and gall tissue three-dimensional model is established, and feature recognition analysis is performed to obtain a liver and gall tissue feature data set; carrying out comprehensive analysis to obtain a liver tissue health index and a bile tissue health index; the method comprises the following steps: constructing a complete three-dimensional model of the liver and gall tissues, extracting multi-dimensional features of the liver and gall tissues, performing comprehensive analysis to obtain a comprehensive health index of the liver and gall tissues, performing judgment and analysis on the comprehensive health index and a preset comprehensive health evaluation interval of the liver and gall tissues, and performing health marking on the liver and gall tissues according to a judgment and analysis result. The precision and comprehensiveness of liver and gallbladder health assessment are remarkably improved, nonlinear synthesis is performed on the liver tissue health index and the gallbladder tissue health index obtained through analysis, and the calculated liver and gallbladder tissue comprehensive health index can comprehensively assess the overall health state of the liver and gallbladder system.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to a method and system for processing hepatobiliary CT images. Background Art

[0002] In the field of modern medical image analysis and hepatobiliary health assessment, accurate analysis of hepatobiliary tissue is a critical and widely applicable research topic. Traditional hepatobiliary health assessment methods mainly rely on simple parameter analysis of single indicators such as volume and density. However, these methods usually have problems such as single indicators and insufficient sensitivity to complex disease characteristics. In many practical applications, methods based on multidimensional features and comprehensive analysis have become a more attractive choice due to their comprehensiveness and accuracy.

[0003] Although the evaluation methods based on multidimensional features have the above advantages, they still face many challenges in practical applications. For example, traditional methods only focus on the characteristics of a single region of the liver or gallbladder, ignoring the coordinated changes of the hepatobiliary system as a whole under pathological conditions. In addition, many evaluation methods rely on simple weighted models and fail to fully capture the nonlinear correlation between hepatobiliary tissue characteristics, which easily leads to poor performance on complex lesions and is difficult to meet the needs of comprehensive health assessment.

[0004] Traditional technologies also face limitations in feature extraction and analysis when processing three-dimensional models of hepatobiliary tissue. Conventional methods lack innovative analytical methods for deep features such as the geometric complexity of the liver and metabolically active areas. At the same time, insufficient exploration of features such as the distribution of gallstones and the uniformity of gallbladder wall thickness can easily lead to evaluation results that are difficult to fully reflect the actual health status of hepatobiliary tissue. In addition, when synthesizing the hepatobiliary health index, traditional technologies fail to effectively integrate multidimensional features, which can easily lead to deviations and affect the ability to support early diagnosis and precise treatment of the disease. Summary of the invention

[0005] In view of the technical problems existing in the prior art, the present invention provides a hepatobiliary CT image processing method and system, which solves the problems in the prior art of single hepatobiliary tissue evaluation, lack of nonlinear feature analysis and neglect of the overall coordinated changes of the hepatobiliary system.

[0006] The technical solution of the present invention to solve the above technical problems is as follows: a hepatobiliary CT image processing method, comprising the following steps: obtaining a hepatobiliary CT image and establishing a three-dimensional model of hepatobiliary tissue, wherein the hepatobiliary CT image includes a plurality of two-dimensional slice images and a slice depth value of each two-dimensional slice image; performing feature recognition analysis on the three-dimensional model of the hepatobiliary tissue to obtain a hepatobiliary tissue feature data set; performing a comprehensive analysis on the hepatobiliary tissue feature data set to obtain a liver tissue health index and a gallbladder tissue health index; performing a comprehensive analysis on the liver tissue health index and the gallbladder tissue health index to obtain a comprehensive hepatobiliary tissue health index, and performing a judgment analysis with a preset comprehensive hepatobiliary tissue health assessment interval, and marking the hepatobiliary tissue for health according to the judgment analysis result; wherein the specific formula for calculating the comprehensive hepatobiliary tissue health index is as follows: ;in, is the comprehensive health index of liver and gallbladder tissue, is a natural constant, is the liver tissue health index, is the liver tissue weight coefficient stored in the database, is the bile tissue health index, is the bile tissue weight coefficient stored in the database, .

[0007] Furthermore, the two-dimensional slice image includes pixel values ​​and two-dimensional coordinates of several hepatobiliary pixel points, the hepatobiliary tissue feature data set includes a liver tissue feature data set and a gallbladder tissue feature data set, the liver tissue feature data set includes a geometric complexity index, a grayscale fluctuation index, and a metabolically active area index of the liver tissue area, and the gallbladder tissue feature data set includes a gallbladder wall thickness uniformity index, a gallbladder stone feature index, and a gallbladder grayscale variation index of the gallbladder tissue area.

[0008] Furthermore, the specific steps of establishing a three-dimensional model of hepatobiliary tissue are as follows: each two-dimensional slice image is processed to a uniform size, and each two-dimensional slice image is arranged in descending order based on the slice depth value of each two-dimensional slice image; a plurality of two-dimensional slice images arranged in descending order are stacked, and slice image interpolation is performed based on a preset interpolation rule to obtain a three-dimensional model of hepatobiliary tissue, wherein the three-dimensional model of hepatobiliary tissue includes a plurality of hepatobiliary voxel points, and a voxel value and a three-dimensional coordinate value of each hepatobiliary voxel point.

[0009] Furthermore, the specific steps of performing slice image interpolation processing based on preset interpolation rules are as follows: based on the slice depth value of each two-dimensional slice image, the slice depth spacing between adjacent two-dimensional slice images and the preset slice depth spacing threshold are judged and analyzed; if the slice depth spacing between adjacent two-dimensional slice images is higher than the preset slice depth spacing threshold, an interpolated slice image is inserted between the adjacent two-dimensional slice images, and the interpolated slice image includes a number of interpolated liver and gallbladder pixel points.

[0010] Furthermore, the specific steps of performing feature recognition analysis on the three-dimensional model of hepatobiliary tissue to obtain a hepatobiliary tissue feature data set are as follows: grayscale processing is performed on the voxel value of each hepatobiliary voxel point in the three-dimensional model of hepatobiliary tissue to obtain the grayscale voxel value of each hepatobiliary voxel point in the three-dimensional model of hepatobiliary tissue; based on the preset liver tissue grayscale voxel interval and gallbladder tissue grayscale voxel interval, the grayscale voxel value of each hepatobiliary voxel point in the three-dimensional model of hepatobiliary tissue is binarized to obtain the liver tissue region and gallbladder tissue region in the three-dimensional model of hepatobiliary tissue, wherein the liver tissue region includes several liver voxel points and the grayscale voxel value of each liver voxel point, the bile tissue area includes a number of bile voxel points and the grayscale voxel value of each bile voxel point; the liver tissue areas are comprehensively analyzed to obtain the geometric complexity index, grayscale fluctuation index and metabolic active area index of the liver tissue area, and a comprehensive analysis is performed to obtain the liver tissue health index; the bile tissue areas are comprehensively analyzed to obtain the gallbladder wall thickness uniformity index, gallbladder stone characteristic index and gallbladder grayscale change index of the bile tissue area, and a comprehensive analysis is performed to obtain the bile tissue health index.

[0011] Furthermore, the specific formula for calculating the liver tissue health index is as follows: ;in, is the liver tissue health index, is the geometric complexity index of the liver tissue area, is the geometric coefficient of liver tissue stored in the database, is the grayscale fluctuation index of the liver tissue area, is the grayscale fluctuation coefficient of liver tissue stored in the database, is the metabolically active area index of the liver tissue region, It is the metabolic activity coefficient of liver tissue stored in the database.

[0012] Furthermore, the specific steps for obtaining the geometric complexity index, grayscale fluctuation index, and metabolically active area index of the liver tissue region are as follows: perform surface area analysis and boundary analysis on the liver tissue region, respectively, to obtain the liver tissue surface area value and the liver tissue boundary length value of the liver tissue region, and perform comprehensive analysis to obtain the geometric complexity index of the liver tissue region; read the grayscale voxel value of each liver voxel point in the liver tissue region, and perform comprehensive analysis to obtain the grayscale fluctuation index of the liver tissue region; compare and analyze the grayscale voxel value of each liver voxel point in the liver tissue region with the preset metabolically active grayscale voxel value, and take the liver voxel point with a grayscale voxel value higher than the preset metabolically active grayscale voxel value as the metabolically active liver voxel point, and perform comprehensive analysis to obtain the metabolically active area index of the liver tissue region.

[0013] Furthermore, the specific formula for calculating the gallbladder tissue health index is as follows: ;in, is the bile tissue health index, is the gallbladder wall thickness uniformity index in the gallbladder tissue area, is the bile tissue thickness uniformity coefficient stored in the database, is the gallbladder stone characteristic index in the gallbladder tissue area, is the characteristic coefficient of gallstones stored in the database, is the gallbladder grayscale change index in the gallbladder tissue area, is the grayscale variation coefficient of bile tissue stored in the database.

[0014] Furthermore, the specific steps of obtaining the gallbladder wall thickness uniformity index, gallbladder stone characteristic index, and gallbladder grayscale change index of the gallbladder tissue area are as follows: for the gallbladder tissue area, arbitrarily select a number of gallbladder wall measurement points, and analyze the gallbladder wall thickness value of each gallbladder wall measurement point respectively; compare and analyze the gallbladder wall thickness value of each gallbladder wall measurement point to obtain the maximum gallbladder wall thickness and the minimum gallbladder wall thickness of the gallbladder tissue area; compare the gallbladder wall thickness value of each gallbladder wall measurement point, the maximum gallbladder wall thickness and the minimum gallbladder wall thickness of the gallbladder tissue area Comprehensive analysis was performed to obtain the uniformity index of the gallbladder wall thickness in the gallbladder tissue area; the grayscale voxel value of each bile voxel point in the gallbladder tissue area was compared with the preset grayscale voxel value of gallbladder nodules, and the bile voxel points with grayscale voxel values ​​higher than the preset grayscale voxel value of gallbladder nodules were used as the characteristic voxel points of gallbladder stones, and the number of characteristic voxel points of gallbladder stones was used as the characteristic index of gallbladder stones in the gallbladder tissue area; the grayscale voxel value of each bile voxel point in the gallbladder tissue area was read, and a comprehensive analysis was performed to obtain the grayscale fluctuation index of the gallbladder tissue area.

[0015] A hepatobiliary CT image processing system comprises: a three-dimensional model building module, a feature recognition and analysis module, a health analysis module, a comprehensive analysis module, and a judgment and analysis module; the three-dimensional model building module is used to obtain a hepatobiliary CT image and establish a three-dimensional model of hepatobiliary tissue, wherein the hepatobiliary CT image comprises a plurality of two-dimensional slice images and a slice depth value of each two-dimensional slice image; the feature recognition and analysis module is used to perform feature recognition and analysis on the three-dimensional model of the hepatobiliary tissue to obtain a hepatobiliary tissue feature data set; the health analysis module is used to perform a comprehensive analysis on the hepatobiliary tissue feature data set to obtain a liver tissue health index and a gallbladder tissue health index; the comprehensive analysis module is used to perform a comprehensive analysis on the liver tissue health index and the gallbladder tissue health index to obtain a hepatobiliary tissue comprehensive health index; the judgment and analysis module is used to perform a judgment and analysis on the hepatobiliary tissue comprehensive health index and a preset hepatobiliary tissue comprehensive health assessment interval, and mark the hepatobiliary tissue as healthy according to the judgment and analysis result.

[0016] The beneficial effects of the present invention are as follows: by constructing a complete three-dimensional model of hepatobiliary tissue and extracting multidimensional features, the method significantly improves the accuracy and comprehensiveness of hepatobiliary health assessment. First, by uniformly sizing, deep sorting, stacking and interpolating two-dimensional slice images, the generated three-dimensional model retains the spatial structure and anatomical information of the hepatobiliary tissue, effectively eliminates the problem of data discontinuity between slices, and provides an accurate data basis for subsequent analysis. Secondly, according to the different physiological characteristics of hepatobiliary tissue, the geometric complexity index, grayscale fluctuation index, metabolically active area index of liver tissue, as well as the wall thickness uniformity index, stone characteristic index and grayscale variation index of gallbladder are designed. These features comprehensively reflect the health status of hepatobiliary tissue from multiple dimensions of morphology, density and function, and can sensitively capture early signals of complex lesions. Finally, by nonlinearly integrating the liver tissue health index and the gallbladder tissue health index, the calculated hepatobiliary tissue comprehensive health index can comprehensively evaluate the overall health status of the hepatobiliary system and flexibly adapt to clinical needs under different pathological conditions. This method has shown significant practicality and scientific value in early disease screening, diagnosis and treatment plan formulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a flow chart of a method for processing hepatobiliary CT images according to the present invention.

[0018] Figure 2 The present invention is a flowchart of the specific steps for establishing a three-dimensional model of liver and gallbladder tissue in a method for processing liver and gallbladder CT images.

[0019] Figure 3 This is a block diagram of a hepatobiliary CT image processing system of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0021] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0022] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0023] The overall idea of ​​the problem in the embodiment of this application is as follows: First, hepatobiliary CT images were acquired and a three-dimensional model of hepatobiliary tissue was established. This included uniformly resizing the two-dimensional slice images, sorting and stacking them by slice depth values, and generating a three-dimensional model based on preset interpolation rules. The model included hepatobiliary voxel points, their voxel values, and three-dimensional coordinate values.

[0024] Next, feature recognition analysis is performed on the three-dimensional model to separate the liver tissue and bile tissue regions: this includes dividing the three-dimensional model into liver tissue regions and bile tissue regions through grayscale value processing and binarization, and extracting voxel data of each region, including grayscale voxel value, voxel position, etc.

[0025] Finally, comprehensive feature analysis was performed on the liver tissue and gallbladder tissue regions respectively: including extracting the geometric complexity index, grayscale fluctuation index, and metabolically active area index from the liver tissue region to calculate the liver tissue health index; extracting the gallbladder wall thickness uniformity index, gallbladder stone characteristic index, and grayscale variation index from the gallbladder tissue region to calculate the gallbladder tissue health index. By comprehensively analyzing the liver tissue health index and gallbladder tissue health index, the comprehensive health index of the liver and gallbladder tissues was calculated and evaluated.

[0026] See also Figure 1 The embodiment of the present invention provides a technical solution: a method for processing hepatobiliary CT images, comprising the following steps: obtaining a hepatobiliary CT image and establishing a three-dimensional model of hepatobiliary tissue, wherein the hepatobiliary CT image includes a plurality of two-dimensional slice images and a slice depth value of each two-dimensional slice image (i.e., a Z coordinate value, with the slice direction as the Z coordinate axis and the first slice position as the coordinate axis origin); performing feature recognition analysis on the three-dimensional model of hepatobiliary tissue to obtain a hepatobiliary tissue feature data set; performing comprehensive analysis on the hepatobiliary tissue feature data set to obtain a liver tissue health index and a gallbladder tissue health index; performing comprehensive analysis on the liver tissue health index and the gallbladder tissue health index to obtain a hepatobiliary tissue health index. The liver and gallbladder tissue comprehensive health index is determined and compared with the preset liver and gallbladder tissue comprehensive health assessment interval, and the liver and gallbladder tissue is marked healthy according to the judgment and analysis results. The specific process is: if the liver and gallbladder tissue comprehensive health index is within the preset liver and gallbladder tissue comprehensive health assessment interval, the liver tissue area and the gallbladder tissue area are marked as normal; if the liver and gallbladder tissue comprehensive health index is outside the preset liver and gallbladder tissue comprehensive health assessment interval, it is determined whether the liver tissue health index and the gallbladder tissue health index are respectively within the preset liver tissue health assessment interval and the gallbladder tissue health assessment interval, and the health index outside the assessment interval is regarded as an abnormal area and marked.

[0027] Among them, the specific formula for calculating the comprehensive health index of liver and gallbladder tissue is as follows: ;in, is the comprehensive health index of liver and gallbladder tissue, is a natural constant, and in this embodiment, its value is 2.718. is the liver tissue health index, is the liver tissue weight coefficient stored in the database, is the bile tissue health index, is the bile tissue weight coefficient stored in the database, .

[0028] Among them, the implementation example of calculating the comprehensive health index of liver and gallbladder tissue is as follows, and the following data are available: The liver tissue health index is: 0.824.

[0029] The liver tissue weight coefficient stored in the database is: 0.68.

[0030] The gallbladder tissue health index is: 0.645.

[0031] The bile tissue weight coefficient stored in the database is 0.32.

[0032] The constant of nature is: 2.718.

[0033] Substituting the above data into the specific formula for calculating the comprehensive health index of liver and gallbladder tissue, we get: Hepatobiliary tissue comprehensive health index = 1 / (1+e -（0.68*0.824+0.32*0.645） )≈0683.

[0034] The two-dimensional slice image includes the pixel values ​​and two-dimensional coordinates of several hepatobiliary pixel points. The hepatobiliary tissue feature data set includes the liver tissue feature data set and the gallbladder tissue feature data set. The liver tissue feature data set includes the geometric complexity index, grayscale fluctuation index, and metabolic active area index of the liver tissue area. The gallbladder tissue feature data set includes the gallbladder wall thickness uniformity index, gallstone feature index, and gallbladder grayscale change index of the gallbladder tissue area.

[0035] Specifically, Figure 2 As shown, the specific steps of establishing the three-dimensional model of hepatobiliary tissue are as follows: each two-dimensional slice image is processed to a uniform size, and each two-dimensional slice image is arranged in descending order based on the slice depth value of each two-dimensional slice image; a plurality of two-dimensional slice images arranged in descending order are stacked, and slice image interpolation is performed based on a preset interpolation rule to obtain a three-dimensional model of hepatobiliary tissue, wherein the three-dimensional model of hepatobiliary tissue includes a plurality of hepatobiliary voxel points, and a voxel value and a three-dimensional coordinate value of each hepatobiliary voxel point.

[0036] The specific steps of performing slice image interpolation processing based on a preset interpolation rule are as follows: based on the slice depth value of each two-dimensional slice image, the slice depth spacing between adjacent two-dimensional slice images and the preset slice depth spacing threshold are judged and analyzed; if the slice depth spacing between adjacent two-dimensional slice images is higher than the preset slice depth spacing threshold, an interpolated slice image is inserted between the adjacent two-dimensional slice images, and the interpolated slice image includes a number of interpolated liver and gallbladder pixel points.

[0037] The specific formula for calculating the pixel value of each interpolated liver and gallbladder pixel in the interpolated slice image is as follows: ;in, is the interpolated slice image Interpolated pixel values ​​of liver and gallbladder pixels, is the first interpolation coefficient stored in the database, is the second interpolation coefficient stored in the database, , is the first two-dimensional slice image in the adjacent two-dimensional slice image. The pixel value of the liver and gallbladder pixels, is the first two-dimensional slice image in the second two-dimensional slice image in the adjacent two-dimensional slice image. The pixel value of the liver and gallbladder pixels, =1, 2, 3, …, , is the number of interpolated liver and gallbladder pixels in the interpolated slice image.

[0038] In this implementation scheme, by arranging the depth values ​​of the two-dimensional slice images in descending order and stacking them, the cross-sectional data of the hepatobiliary tissue are orderly combined, and reconstruction from two-dimensional to three-dimensional is achieved, the spatial structure and anatomical information of the hepatobiliary tissue are retained, and a complete three-dimensional data basis is provided for subsequent feature analysis. The introduction of interpolation rules solves the problem of excessive depth spacing between adjacent slices. By inserting interpolated slice images, the gaps between data are filled, so that the three-dimensional model has higher continuity and smoothness between slices, thereby improving the fineness of the three-dimensional model. By uniformly sizing each two-dimensional slice image, slice data of different resolutions or pixel sizes can be analyzed for consistency, enhancing the universality and flexibility of the algorithm. , which can adapt to different CT devices and image formats. The calculation of each pixel value in the interpolated slice is based on the pixel value of the adjacent slice and the preset interpolation coefficient, which is calculated by the formula, so that the interpolated image can more accurately reflect the actual tissue density changes, laying a data foundation for the accurate extraction and analysis of hepatobiliary regional features. The complete and continuous three-dimensional model provides reliable data support for subsequent feature recognition (such as liver metabolic areas, gallbladder stone characteristics, etc.) and health assessment, and can more accurately reflect the overall health status of hepatobiliary tissue. By dynamically adjusting the interpolation rules (such as depth spacing threshold and interpolation coefficient), the three-dimensional model can be flexibly optimized according to different clinical needs, thereby better supporting personalized diagnosis and treatment planning.

[0039] Specifically, the specific steps of performing feature recognition analysis on the three-dimensional model of hepatobiliary tissue to obtain the hepatobiliary tissue feature data set are as follows: grayscale processing is performed on the voxel value of each hepatobiliary voxel point in the three-dimensional model of hepatobiliary tissue to obtain the grayscale voxel value of each hepatobiliary voxel point in the three-dimensional model of hepatobiliary tissue; based on the preset liver tissue grayscale voxel interval and gallbladder tissue grayscale voxel interval, the grayscale voxel value of each hepatobiliary voxel point in the three-dimensional model of hepatobiliary tissue is binarized to obtain the liver tissue area and gallbladder tissue area in the three-dimensional model of hepatobiliary tissue, and the liver tissue area includes several The liver voxel points and the grayscale voxel value of each liver voxel point are included in the bile tissue area. The bile tissue area includes several bile voxel points and the grayscale voxel value of each bile voxel point. The liver tissue areas are comprehensively analyzed to obtain the geometric complexity index, grayscale fluctuation index and metabolic active area index of the liver tissue area, and a comprehensive analysis is performed to obtain the liver tissue health index. The bile tissue areas are comprehensively analyzed to obtain the gallbladder wall thickness uniformity index, gallbladder stone characteristic index and gallbladder grayscale change index of the bile tissue area, and a comprehensive analysis is performed to obtain the bile tissue health index.

[0040] The specific formula for calculating the liver tissue health index is as follows: ;in, is the liver tissue health index, is the geometric complexity index of the liver tissue area, is the geometric coefficient of liver tissue stored in the database, is the grayscale fluctuation index of the liver tissue area, is the grayscale fluctuation coefficient of liver tissue stored in the database, is the metabolically active area index of the liver tissue region, It is the metabolic activity coefficient of liver tissue stored in the database.

[0041] The specific formula for calculating the bile tissue health index is as follows: ;in, is the bile tissue health index, is the gallbladder wall thickness uniformity index in the gallbladder tissue area, is the bile tissue thickness uniformity coefficient stored in the database, is the gallbladder stone characteristic index in the gallbladder tissue area, is the characteristic coefficient of gallstones stored in the database, is the gallbladder grayscale change index in the gallbladder tissue area, is the grayscale variation coefficient of bile tissue stored in the database.

[0042] In this implementation scheme, by grayscale processing and regional segmentation of voxel points in the three-dimensional model of hepatobiliary tissue, the core features of the liver tissue region and the gallbladder tissue region are extracted respectively, which comprehensively reflects the health status of the hepatobiliary tissue. The feature extraction process is meticulous and multidimensional, and the geometric morphology, density distribution and functional characteristics of the liver and gallbladder can be accurately quantified, which is helpful to comprehensively evaluate the health level of the hepatobiliary tissue. The geometric morphology complexity index, grayscale fluctuation index and metabolically active area index of the liver tissue focus on reflecting the structural integrity, density uniformity and distribution characteristics of the functional activity area of ​​the liver. The gallbladder wall thickness uniformity index, gallstone characteristic index and grayscale change index of the gallbladder tissue focus on the structural stability, pathological characteristics and internal density changes of the gallbladder. This targeted feature analysis design ensures that the health index of each tissue can accurately reflect its characteristics and possible lesions. By introducing multiple features and focusing on comprehensive feature analysis, the health index of liver and gallbladder tissues is expanded from a single feature evaluation to a multi-dimensional comprehensive evaluation. The calculation formula of the health index not only takes into account the weight of each feature, but also enhances the accuracy of the evaluation results through the relationship between features, avoiding misjudgments that may be caused by a single feature. The calculation formula of the health index combines each feature with its importance coefficient, making the model scientific and flexible: through the weight coefficients such as the geometric morphology coefficient and the grayscale fluctuation coefficient stored in the database, it ensures that the contribution of each feature to the health index is in line with actual medical significance; in different application scenarios, the weight coefficient can be adjusted according to specific needs to adapt to different This method can meet different assessment needs and improve the applicability of the assessment model. By refining the feature analysis of liver and gallbladder tissues, this method can capture early lesions in the tissues (such as liver fibrosis, fatty liver, cholecystitis or stone formation) and their potential health risks. By quantifying the health index, it provides a scientific basis for early diagnosis, which helps doctors to accurately formulate treatment plans and optimize treatment effects. This health index calculation method based on multidimensional features can efficiently process large-scale hepatobiliary CT image data, facilitate batch analysis and health screening in clinical practice, and significantly improve the efficiency and practicality of medical image analysis. Through the construction of a three-dimensional model of hepatobiliary tissue, automatic feature extraction and health index calculation, this method provides a technical basis for the automated analysis and intelligent diagnosis of medical images, which helps to promote the development of medical technology.

[0043] Specifically, the specific steps for obtaining the geometric complexity index, grayscale fluctuation index, and metabolically active area index of the liver tissue region are as follows: surface area analysis and boundary analysis are performed on the liver tissue region respectively (the surface mesh of the liver can be obtained by extracting boundary points, i.e., the contact points between the liver and the surrounding tissues, from the three-dimensional voxel model of the liver. Then, the area calculation formula of the triangular mesh is used, i.e., the area is equal to half of the cross product value of the two edge vectors of the triangle, and the sum of the areas of these surface triangles is calculated to obtain the surface area of ​​the liver. At the same time, the boundary length is calculated by extracting the edge contour of the liver region on each two-dimensional slice and calculating the distance between these contour points. Finally, The boundary lengths of all slices are accumulated to obtain the total boundary length of the liver), the liver tissue surface area value and the liver tissue boundary length value of the liver tissue area are obtained, and a comprehensive analysis is performed to obtain the geometric complexity index of the liver tissue area; the grayscale voxel value of each liver voxel point in the liver tissue area is read, and a comprehensive analysis is performed to obtain the grayscale fluctuation index of the liver tissue area; the grayscale voxel value of each liver voxel point in the liver tissue area is compared and analyzed with the preset metabolically active grayscale voxel value, and the liver voxel point with a grayscale voxel value higher than the preset metabolically active grayscale voxel value is taken as the metabolically active liver voxel point, and a comprehensive analysis is performed to obtain the metabolically active regional index of the liver tissue area.

[0044] The specific formulas for calculating the geometric complexity index, grayscale fluctuation index, and metabolically active area index of the liver tissue region are as follows: ;in, is the geometric complexity index of the liver tissue area, is the liver tissue surface area value of the liver tissue area, is the liver tissue boundary length value of the liver tissue area, is the grayscale fluctuation index of the liver tissue area, The first The grayscale voxel value of the liver voxel point, is the metabolically active area index of the liver tissue region, The first The grayscale voxel value of the metabolically active liver voxel point, =1, 2, 3, …, , is the number of liver voxels in the liver tissue area, =1, 2, 3, …, , It is the number of metabolically active liver voxels in the liver tissue area.

[0045] In this embodiment, the geometric morphological characteristics of liver tissue are accurately characterized through surface area analysis and boundary length analysis. The surface area value of liver tissue reflects the complexity of its external contour, and the boundary length value reveals the morphological changes of liver tissue on two-dimensional slices. These geometric characteristics are further integrated into a geometric morphological complexity index, which can effectively identify whether the liver has morphological abnormalities (such as surface irregularities caused by liver fibrosis or cirrhosis). By reading the grayscale value of liver voxel points and calculating the grayscale fluctuation index, the fluctuation of regional density distribution of liver tissue is quantified. Healthy liver tissue usually has uniform density, while lesions such as fatty liver and inflammation can cause a significant increase in density fluctuation. This feature can sensitively reflect the density changes inside liver tissue, which is helpful for detecting early lesions, especially some hidden lesions that are difficult to identify with the naked eye. By comparing the voxel grayscale value with the preset metabolically active grayscale threshold, the metabolically active areas of liver tissue are identified, and the metabolically active area index is calculated. The size and distribution of can reflect the functional state of the liver, help identify local metabolic abnormalities (such as reduced local activity caused by inflammation, tumors or insufficient blood flow), and provide a more functional reference for disease diagnosis. The geometric complexity index is calculated through a comprehensive analysis of surface area and boundary length, which can balance the complexity of the external morphology and internal contour of liver tissue and adapt to various pathological morphologies. The grayscale fluctuation index adopts a combination of point-by-point analysis and comprehensive evaluation to sensitively capture the distribution characteristics of grayscale values ​​and avoid local abnormalities that may be missed by simple averaging. The metabolically active area index is based on the screening and comprehensive analysis of active voxel points, directly quantifies the activity of metabolic function, and is particularly suitable for reflecting the dynamic functional state of the liver. Through the extraction and quantitative analysis of multidimensional features, this part of the method can reflect the health status of liver tissue from multiple dimensions of morphology, density and function. Compared with the traditional single indicator evaluation, this method can provide richer information and improve the accuracy and comprehensiveness of health assessment.

[0046] Specifically, the specific steps for obtaining the gallbladder wall thickness uniformity index, gallbladder stone characteristic index, and gallbladder grayscale change index of the gallbladder tissue region are as follows: for the gallbladder tissue region, arbitrarily select a number of gallbladder wall measurement points, and analyze the gallbladder wall thickness value of each gallbladder wall measurement point (i.e., the distance value between the outer surface point and the inner surface point); compare and analyze the gallbladder wall thickness value of each gallbladder wall measurement point to obtain the maximum gallbladder wall thickness value and the minimum gallbladder wall thickness value of the gallbladder tissue region; compare the gallbladder wall thickness value of each gallbladder wall measurement point, the maximum gallbladder wall thickness value and the minimum gallbladder wall thickness value of the gallbladder tissue region The minimum value of wall thickness is comprehensively analyzed to obtain the uniformity index of gallbladder wall thickness in the gallbladder tissue area; the grayscale voxel value of each bile voxel point in the gallbladder tissue area is compared and analyzed with the preset grayscale voxel value of gallbladder nodules, and the bile voxel points with grayscale voxel values ​​higher than the preset grayscale voxel value of gallbladder nodules are taken as the characteristic voxel points of gallbladder stones, and the number of characteristic voxel points of gallbladder stones is taken as the characteristic index of gallbladder stones in the gallbladder tissue area; the grayscale voxel value of each bile voxel point in the gallbladder tissue area is read and comprehensively analyzed to obtain the grayscale fluctuation index of the gallbladder tissue area.

[0047] The specific formulas for calculating the gallbladder wall thickness uniformity index and the gallbladder grayscale variation index in the gallbladder tissue area are as follows: ;in, is the gallbladder wall thickness uniformity index in the gallbladder tissue area, is the maximum gallbladder wall thickness in the bile tissue area, is the minimum gallbladder wall thickness in the bile tissue area, The first The gallbladder wall thickness value at each gallbladder wall measurement point, is the gallbladder grayscale change index in the gallbladder tissue area, The first The grayscale voxel value of each voxel point, =1, 2, 3, …, , is the number of measurement points on the gallbladder wall, =1, 2, 3, ..., , is the number of cholangiocarcinoma points in the bile tissue area.

[0048] In this embodiment, by selecting several gallbladder wall measurement points and analyzing the wall thickness value of each point, the thickness distribution characteristics of the gallbladder wall can be fully reflected. The uniformity of the gallbladder wall thickness is closely related to the health status, and unevenness may indicate cholecystitis or other lesions. The maximum and minimum values ​​of the gallbladder wall thickness are calculated, and the gallbladder wall thickness uniformity index is obtained through comprehensive analysis, which can quantify the variation range of the gallbladder wall thickness distribution and help identify the lesion area. This feature can sensitively reflect the early pathological changes of the gallbladder wall, such as local thickening caused by gallbladder inflammation, and provide a scientific basis for the early diagnosis of the disease. By comparing the grayscale value of each voxel in the gallbladder area with the preset grayscale threshold of gallstones, the high-density stone area is identified, and the number of stone characteristic voxel points is used as the gallbladder stone characteristic index. The gallbladder stone characteristic index can directly quantify the severity of the stone (such as the number of stones) and provide a quantitative basis for further treatment (such as whether surgery is needed). This method is based on simple and intuitive voxel grayscale comparison, and the calculation is efficient and accurate. It can quickly identify the characteristics of stones and is particularly suitable for the screening of large-scale gallbladder imaging data. By reading the grayscale value of each voxel point in the gallbladder area and performing a comprehensive analysis, the gallbladder grayscale change index is calculated. The grayscale distribution of a healthy gallbladder is usually relatively uniform, while cholecystitis, abnormal bile concentration or other lesions may cause abnormal grayscale distribution. The grayscale change index can quantify the degree of grayscale fluctuation, reflecting the health of the bile in the gallbladder, and helping to identify density unevenness caused by abnormal bile flow or inflammation. This feature can capture tiny abnormalities in the gallbladder from the perspective of density change. Combined with other features, it can comprehensively reflect the health status of the gallbladder. By extracting features in three dimensions: gallbladder wall thickness, stone characteristics and grayscale change, it covers the three core health indicators of gallbladder morphology, pathology and function. Different features complement each other, and the health of the gallbladder can be comprehensively evaluated from multiple angles to avoid misjudgment that may be caused by a single indicator. This multidimensional analysis method greatly improves the accuracy and comprehensiveness of gallbladder health assessment and is suitable for the accurate diagnosis of complex lesions.

[0049] See also Figure 3A hepatobiliary CT image processing system includes: a three-dimensional model building module, a feature recognition and analysis module, a health analysis module, a comprehensive analysis module, and a judgment analysis module; the three-dimensional model building module is used to obtain a hepatobiliary CT image and establish a three-dimensional model of hepatobiliary tissue, the hepatobiliary CT image includes a plurality of two-dimensional slice images and a slice depth value of each two-dimensional slice image; the feature recognition and analysis module is used to perform feature recognition and analysis on the three-dimensional model of the hepatobiliary tissue to obtain a hepatobiliary tissue feature data set; the health analysis module is used to perform a comprehensive analysis on the hepatobiliary tissue feature data set to obtain a liver tissue health index and a gallbladder tissue health index; the comprehensive analysis module is used to perform a comprehensive analysis on the liver tissue health index and the gallbladder tissue health index to obtain a hepatobiliary tissue comprehensive health index; the judgment analysis module is used to perform a judgment analysis on the hepatobiliary tissue comprehensive health index and a preset hepatobiliary tissue comprehensive health assessment interval, and mark the hepatobiliary tissue as healthy according to the judgment analysis result.

[0050] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0051] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0052] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0053] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0054] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0055] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0056] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for processing hepatobiliary CT images, characterized in that: The following steps are involved: Acquire a hepatobiliary CT image and establish a three-dimensional model of hepatobiliary tissue, wherein the hepatobiliary CT image includes a plurality of two-dimensional slice images and a slice depth value of each two-dimensional slice image; Perform feature recognition analysis on the three-dimensional model of liver and gallbladder tissue to obtain a liver and gallbladder tissue feature data set; Comprehensively analyze the liver and gallbladder tissue feature data set to obtain the liver tissue health index and gallbladder tissue health index; Comprehensively analyze the liver tissue health index and gallbladder tissue health index to obtain the liver and gallbladder tissue comprehensive health index, and make a judgment analysis with the preset liver and gallbladder tissue comprehensive health assessment interval, and mark the liver and gallbladder tissue health according to the judgment analysis results; Among them, the specific formula for calculating the comprehensive health index of liver and gallbladder tissue is as follows: ； in, is the comprehensive health index of liver and gallbladder tissue, is a natural constant, is the liver tissue health index, is the liver tissue weight coefficient stored in the database, is the bile tissue health index, is the bile tissue weight coefficient stored in the database, .

2. The method for processing hepatobiliary CT images according to claim 1, characterized in that: The two-dimensional slice image includes pixel values ​​and two-dimensional coordinates of a plurality of hepatobiliary pixel points, the hepatobiliary tissue feature data set includes a liver tissue feature data set and a gallbladder tissue feature data set, the liver tissue feature data set includes a geometric complexity index, a grayscale fluctuation index, and a metabolically active area index of a liver tissue region, and the gallbladder tissue feature data set includes a gallbladder wall thickness uniformity index, a gallbladder stone feature index, and a gallbladder grayscale variation index of a gallbladder tissue region.

3. The method for processing hepatobiliary CT images according to claim 1, characterized in that: The specific steps for establishing a three-dimensional model of liver and gallbladder tissue are as follows: Performing uniform size processing on each two-dimensional slice image, and performing descending sorting processing on each two-dimensional slice image based on a slice depth value of each two-dimensional slice image; A plurality of two-dimensional slice images arranged in descending order are stacked, and slice image interpolation is performed based on a preset interpolation rule to obtain a three-dimensional model of hepatobiliary tissue, wherein the three-dimensional model of hepatobiliary tissue includes a plurality of hepatobiliary voxel points, and a voxel value and a three-dimensional coordinate value of each hepatobiliary voxel point.

4. The method for processing hepatobiliary CT images according to claim 3, characterized in that: The specific steps of performing slice image interpolation processing based on the preset interpolation rules are as follows: Based on the slice depth value of each two-dimensional slice image, the slice depth interval between adjacent two-dimensional slice images is judged and analyzed with a preset slice depth interval threshold; If the slice depth spacing between adjacent two-dimensional slice images is higher than a preset slice depth spacing threshold, an interpolated slice image is inserted between the adjacent two-dimensional slice images, and the interpolated slice image includes a plurality of interpolated liver and gallbladder pixel points.

5. The method for processing hepatobiliary CT images according to claim 2, characterized in that: The specific steps for performing feature recognition analysis on the three-dimensional model of liver and gallbladder tissue and obtaining the liver and gallbladder tissue feature data set are as follows: grayscale processing is performed on the voxel value of each hepatobiliary voxel point in the three-dimensional model of hepatobiliary tissue to obtain the grayscale voxel value of each hepatobiliary voxel point in the three-dimensional model of hepatobiliary tissue; Based on the preset liver tissue grayscale voxel interval and gallbladder tissue grayscale voxel interval, the grayscale voxel value of each liver and gallbladder voxel point in the three-dimensional model of the liver and gallbladder tissue is binarized to obtain a liver tissue region and a gallbladder tissue region in the three-dimensional model of the liver and gallbladder tissue, wherein the liver tissue region includes a plurality of liver voxel points and a grayscale voxel value of each liver voxel point, and the gallbladder tissue region includes a plurality of gallbladder voxel points and a grayscale voxel value of each gallbladder voxel point; Comprehensive analysis was performed on liver tissue regions to obtain the geometric complexity index, grayscale fluctuation index, and metabolically active region index of the liver tissue region, and a comprehensive analysis was performed to obtain the liver tissue health index; The gallbladder tissue regions were comprehensively analyzed to obtain the gallbladder wall thickness uniformity index, gallbladder stone characteristic index, and gallbladder grayscale change index of the gallbladder tissue region. A comprehensive analysis was then performed to obtain the gallbladder tissue health index.

6. The method for processing hepatobiliary CT images according to claim 5, characterized in that: The specific formula for calculating the liver tissue health index is as follows: ； in, is the liver tissue health index, is the geometric complexity index of the liver tissue area, is the geometric coefficient of liver tissue stored in the database, is the grayscale fluctuation index of the liver tissue area, is the grayscale fluctuation coefficient of liver tissue stored in the database, is the metabolically active area index of the liver tissue region, It is the metabolic activity coefficient of liver tissue stored in the database.

7. The method for processing hepatobiliary CT images according to claim 5, characterized in that: The specific steps for obtaining the geometric complexity index, grayscale fluctuation index, and metabolically active region index of the liver tissue region are as follows: The surface area analysis and boundary analysis of the liver tissue region are respectively performed to obtain the liver tissue surface area value and liver tissue boundary length value of the liver tissue region, and a comprehensive analysis is performed to obtain the geometric complexity index of the liver tissue region; Read the grayscale voxel value of each liver voxel point in the liver tissue area, and perform comprehensive analysis to obtain the grayscale fluctuation index of the liver tissue area; The grayscale voxel value of each liver voxel point in the liver tissue area is compared and analyzed with the preset metabolically active grayscale voxel value, and the liver voxel points with grayscale voxel values ​​higher than the preset metabolically active grayscale voxel value are regarded as metabolically active liver voxel points. A comprehensive analysis is performed to obtain the metabolically active regional index of the liver tissue area.

8. The method for processing hepatobiliary CT images according to claim 5, characterized in that: The specific formula for calculating the bile tissue health index is as follows: ； in, is the bile tissue health index, is the gallbladder wall thickness uniformity index in the gallbladder tissue area, is the bile tissue thickness uniformity coefficient stored in the database, is the gallbladder stone characteristic index in the gallbladder tissue area, is the characteristic coefficient of gallstones stored in the database, is the gallbladder grayscale change index in the gallbladder tissue area, is the grayscale variation coefficient of bile tissue stored in the database.

9. The method for processing hepatobiliary CT images according to claim 5, characterized in that: The specific steps for obtaining the gallbladder wall thickness uniformity index, gallbladder stone characteristic index, and gallbladder grayscale change index of the gallbladder tissue area are as follows: For the gallbladder tissue area, several gallbladder wall measurement points were randomly selected, and the gallbladder wall thickness value of each gallbladder wall measurement point was analyzed separately; Compare and analyze the gallbladder wall thickness values ​​at each gallbladder wall measurement point to obtain the maximum and minimum gallbladder wall thickness values ​​in the gallbladder tissue area. Comprehensive analysis was performed on the gallbladder wall thickness value at each gallbladder wall measurement point, the maximum gallbladder wall thickness value and the minimum gallbladder wall thickness value in the gallbladder tissue area to obtain the gallbladder wall thickness uniformity index in the gallbladder tissue area. The grayscale voxel value of each bile voxel point in the bile tissue region is compared and analyzed with the preset grayscale voxel value of gallbladder nodules, and the bile voxel point with a grayscale voxel value higher than the preset grayscale voxel value of gallbladder nodules is taken as a characteristic voxel point of gallbladder stones, and the number of characteristic voxel points of gallbladder stones is taken as a characteristic index of gallbladder stones in the bile tissue region; The grayscale voxel value of each bile voxel point in the bile tissue area is read and comprehensively analyzed to obtain the grayscale fluctuation index of the bile tissue area.

10. A hepatobiliary CT image processing system, using the hepatobiliary CT image processing method according to any one of claims 1 to 9, characterized in that: include: Three-dimensional model building module, feature recognition and analysis module, health analysis module, comprehensive analysis module, and judgment analysis module; The three-dimensional model building module is used to obtain a hepatobiliary CT image and build a three-dimensional model of hepatobiliary tissue, wherein the hepatobiliary CT image includes a plurality of two-dimensional slice images and a slice depth value of each two-dimensional slice image; The feature recognition and analysis module is used to perform feature recognition and analysis on the three-dimensional model of hepatobiliary tissue to obtain a hepatobiliary tissue feature data set; The health analysis module is used to perform a comprehensive analysis on the liver and gallbladder tissue characteristic data set to obtain a liver tissue health index and a gallbladder tissue health index; The comprehensive analysis module is used to comprehensively analyze the liver tissue health index and the gallbladder tissue health index to obtain the liver and gallbladder tissue comprehensive health index; The judgment and analysis module is used to judge and analyze the comprehensive health index of the hepatobiliary tissue and the preset comprehensive health assessment interval of the hepatobiliary tissue, and to mark the health of the hepatobiliary tissue according to the judgment and analysis results.

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