A pathological image processing method and system based on lesion target area selection

Abstract

The embodiments of the present invention relate to the technical field of pathological image processing, and specifically disclose a pathological image processing method and system based on lesion target area selection. The embodiments of the present invention obtain a target pathological image and perform image preprocessing; perform fuzzy feature analysis to extract a suspicious lesion image; perform fine segmentation processing to extract a target lesion image; perform lesion classification, grading and quantitative analysis to obtain a lesion analysis report; based on the lesion analysis report, create a target lesion image and a background lesion image, synthesize and display a multi-dimensional lesion image. The system can perform image preprocessing, fuzzy feature analysis, fine segmentation processing, lesion classification, grading and quantitative analysis on the target pathological image, create a target lesion image and a background lesion image, synthesize and visualize a multi-dimensional lesion image, achieve standardized processing of pathological images, improve the integrity of pathological image processing, and the processing process is fully automatic, reducing annotation costs and promoting popularization and use.

Description

Technical Field

[0001] The present invention belongs to the technical field of pathological image processing, and in particular relates to a pathological image processing method and system based on lesion target area selection. Background Art

[0002] Pathology image processing refers to the process of analyzing and processing pathology images using computer technology. In the medical field, especially in pathology, pathology image processing technology is widely used for digital analysis of cell and tissue samples to assist in diagnosis, research, and teaching.

[0003] In the existing technology, the pathology image processing process is usually relatively simple, and there is no standardized processing flow, which easily leads to incomplete pathology image processing. In addition, the selection process of the lesion target area usually requires manual processing, and the cost of manual labeling is high, which affects its promotion and use. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a pathology image processing method and system based on lesion target area selection, aiming to solve the problems raised in the background technology.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] A pathology image processing method based on lesion target area selection, the method specifically comprising the following steps:

[0007] Acquiring a target pathological image, performing image preprocessing on the target pathological image, and generating a standard pathological image;

[0008] Performing fuzzy feature analysis on the standard pathological image, determining a suspicious lesion area from the standard pathological image, and extracting a suspicious lesion image;

[0009] Performing fine segmentation processing on the suspicious lesion image, determining the lesion segmentation boundary, and extracting the target lesion image from the suspicious lesion image;

[0010] Extracting lesion features from the target lesion image to obtain lesion feature data, and performing lesion classification, grading, and quantitative analysis based on the lesion feature data to obtain a lesion analysis report;

[0011] Based on the lesion analysis report, a target lesion image and a background lesion image are created, and a multi-dimensional lesion image is synthesized and visually displayed.

[0012] As a further limitation of the technical solution of the embodiment of the present invention, the acquiring of the target pathological image, performing image preprocessing on the target pathological image, and generating a standard pathological image specifically include the following steps:

[0013] Acquire target pathological images;

[0014] performing noise removal on the target pathological image to generate a denoised pathological image;

[0015] performing contrast enhancement on the denoised pathological image to generate a contrast pathological image;

[0016] The contrast pathology image is standardized to generate a standard pathology image.

[0017] As a further limitation of the technical solution of the embodiment of the present invention, performing fuzzy feature analysis on the standard pathology image, determining the suspicious lesion area from the standard pathology image, and extracting the suspicious lesion image specifically include the following steps:

[0018] Performing overall fuzzy feature analysis on the standard pathological image to obtain fuzzy feature data;

[0019] Based on the fuzzy feature data, locating candidate lesions on the standard pathological image to determine suspicious lesion areas;

[0020] A suspicious lesion image corresponding to the suspicious lesion area is extracted from the standard pathological image.

[0021] As a further limitation of the technical solution of the embodiment of the present invention, the performing fine segmentation processing on the suspicious lesion image, determining the lesion segmentation boundary, and extracting the target lesion image from the suspicious lesion image specifically includes the following steps:

[0022] Performing quality analysis on the suspicious lesion image to determine the lesion image quality;

[0023] Selecting a target cutting algorithm according to the quality of the lesion image;

[0024] Based on the target cutting algorithm, fine segmentation processing is performed on the suspicious lesion image to determine the lesion segmentation boundary;

[0025] From the suspicious lesion image, the target lesion image corresponding to the lesion segmentation boundary is extracted.

[0026] As a further limitation of the technical solution of the embodiment of the present invention, extracting lesion features from the target lesion image, obtaining lesion feature data, and performing lesion classification, grading, and quantitative analysis based on the lesion feature data to obtain a lesion analysis report specifically includes the following steps:

[0027] Extracting lesion features from the target lesion image to obtain lesion feature data;

[0028] Performing lesion classification analysis based on the lesion characteristic data to obtain a lesion classification result;

[0029] Performing lesion grading analysis based on the lesion characteristic data to obtain a lesion grading result;

[0030] Performing lesion quantitative analysis based on the lesion characteristic data to obtain lesion quantitative results;

[0031] The lesion classification result, the lesion grading result and the lesion quantification result are combined to obtain a lesion analysis report.

[0032] As a further limitation of the technical solution of the embodiment of the present invention, the creating of the target lesion image and the background lesion image based on the lesion analysis report, and synthesizing and visually displaying the multi-dimensional lesion image specifically includes the following steps:

[0033] Creating a target lesion image and a background lesion image based on the lesion analysis report;

[0034] Determining a display position and display ratio of the target lesion image;

[0035] performing multi-dimensional processing on the target lesion image and the background lesion image according to the display position and the display ratio to synthesize a multi-dimensional lesion image;

[0036] The multi-dimensional lesion image is visually displayed.

[0037] A pathology image processing system based on lesion target area selection, the system includes a pathology image preprocessing unit, a fuzzy feature analysis unit, a fine segmentation processing unit, a lesion feature extraction unit and a multi-dimensional visualization display unit, wherein:

[0038] A pathology image preprocessing unit, configured to acquire a target pathology image, perform image preprocessing on the target pathology image, and generate a standard pathology image;

[0039] a fuzzy feature analysis unit, configured to perform fuzzy feature analysis on the standard pathology image, determine a suspicious lesion area from the standard pathology image, and extract a suspicious lesion image;

[0040] a fine segmentation processing unit, configured to perform fine segmentation processing on the suspicious lesion image, determine the lesion segmentation boundary, and extract the target lesion image from the suspicious lesion image;

[0041] a lesion feature extraction unit, configured to extract lesion features from the target lesion image, obtain lesion feature data, and perform lesion classification, grading, and quantitative analysis based on the lesion feature data to obtain a lesion analysis report;

[0042] The multi-dimensional visualization display unit is used to create a target lesion image and a background lesion image based on the lesion analysis report, and synthesize and visualize the multi-dimensional lesion image.

[0043] As a further limitation of the technical solution of the embodiment of the present invention, the pathological image preprocessing unit specifically includes:

[0044] An image acquisition module, used for acquiring target pathological images;

[0045] A noise removal module, configured to remove noise from the target pathological image to generate a denoised pathological image;

[0046] A contrast enhancement module, configured to perform contrast enhancement on the denoised pathological image to generate a contrast pathological image;

[0047] The standardization processing module is used to perform standardization processing on the contrast pathology image to generate a standard pathology image.

[0048] As a further limitation of the technical solution of the embodiment of the present invention, the fuzzy feature analysis unit specifically includes:

[0049] A fuzzy feature analysis module, configured to perform overall fuzzy feature analysis on the standard pathological image to obtain fuzzy feature data;

[0050] a lesion candidate positioning module, configured to perform lesion candidate positioning on the standard pathology image based on the fuzzy feature data, and determine a suspicious lesion area;

[0051] The suspicious lesion image extraction module is used to extract the suspicious lesion image corresponding to the suspicious lesion area from the standard pathological image.

[0052] As a further limitation of the technical solution of the embodiment of the present invention, the fine segmentation processing unit specifically includes:

[0053] A quality analysis module, configured to perform quality analysis on the suspicious lesion image to determine the quality of the lesion image;

[0054] A cutting algorithm selection module, configured to select a target cutting algorithm according to the quality of the lesion image;

[0055] A fine segmentation processing module is used to perform fine segmentation processing on the suspicious lesion image based on the target cutting algorithm to determine the lesion segmentation boundary;

[0056] The target lesion image extraction module is used to extract the target lesion image corresponding to the lesion segmentation boundary from the suspicious lesion image.

[0057] Compared with the prior art, the present invention has the following beneficial effects:

[0058] The embodiment of the present invention acquires a target pathology image and performs image preprocessing; performs fuzzy feature analysis to extract a suspicious lesion image; performs fine segmentation processing to extract a target lesion image; performs lesion classification, grading, and quantitative analysis to obtain a lesion analysis report; and based on the lesion analysis report, creates a target lesion image and a background lesion image, synthesizes, and displays a multidimensional lesion image. The system can perform image preprocessing, fuzzy feature analysis, fine segmentation processing, lesion classification, grading, and quantitative analysis on the target pathology image, create a target lesion image and a background lesion image, synthesize, and visualize a multidimensional lesion image, achieve standardized processing of pathology images, improve the integrity of pathology image processing, and fully automate the processing process, reducing annotation costs and promoting widespread use. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention.

[0060] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0061] Figure 2 A flow chart of generating a standard pathological image in the method provided by an embodiment of the present invention is shown.

[0062] Figure 3 A flow chart of extracting suspicious lesion images in the method provided by an embodiment of the present invention is shown.

[0063] Figure 4 A flow chart of extracting a target lesion image in a method provided by an embodiment of the present invention is shown.

[0064] Figure 5 A flow chart of obtaining a lesion analysis report in the method provided in an embodiment of the present invention is shown.

[0065] Figure 6 A flow chart of synthesizing a multi-dimensional lesion image in the method provided by an embodiment of the present invention is shown.

[0066] Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0067] Figure 8 The figure shows a structural block diagram of a pathological image preprocessing unit in a system provided by an embodiment of the present invention.

[0068] Figure 9 The structure block diagram of the fuzzy feature analysis unit in the system provided by the embodiment of the present invention is shown.

[0069] Figure 10The figure shows a structural block diagram of a fine segmentation processing unit in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0071] It is understandable that in the existing technology, the pathology image processing process is usually relatively simple, and there is no standardized processing flow, which easily leads to incomplete pathology image processing. In addition, the selection process of the lesion target area usually requires manual processing, and the cost of manual labeling is high, which affects its promotion and use.

[0072] To address the above-mentioned problems, an embodiment of the present invention acquires a target pathology image, performs image preprocessing on the target pathology image, and generates a standard pathology image; performs fuzzy feature analysis on the standard pathology image, determines the suspicious lesion area from the standard pathology image, and extracts the suspicious lesion image; performs fine segmentation processing on the suspicious lesion image, determines the lesion segmentation boundary, and extracts the target lesion image from the suspicious lesion image; performs lesion feature extraction on the target lesion image, obtains lesion feature data, and performs lesion classification, grading, and quantitative analysis based on the lesion feature data to obtain a lesion analysis report; based on the lesion analysis report, creates a target lesion image and a background lesion image, synthesizes, and visualizes a multi-dimensional lesion image. The present invention is capable of performing image preprocessing, fuzzy feature analysis, fine segmentation processing, lesion classification, grading, and quantitative analysis on the target pathology image, creates a target lesion image and a background lesion image, synthesizes, and visualizes a multi-dimensional lesion image, achieves standardized processing of pathology images, improves the integrity of pathology image processing, and fully automates the processing process, reducing annotation costs and promoting widespread use.

[0073] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0074] Specifically, a pathology image processing method based on lesion target area selection includes the following steps:

[0075] Step S101 : acquiring a target pathological image, performing image preprocessing on the target pathological image, and generating a standard pathological image.

[0076] In an embodiment of the present invention, Gaussian filtering and / or median filtering are used to remove random noise and salt and pepper noise from the target pathological image to generate a denoised pathological image. Then, histogram equalization or CLAHE and other methods are used to adjust the contrast of the denoised pathological image to generate a contrast pathological image. Thereafter, the contrast pathological image is standardized in size, resolution, etc. to generate a standard pathological image.

[0077] Specifically, Figure 2 A flow chart of generating a standard pathological image in the method provided by an embodiment of the present invention is shown.

[0078] In a preferred embodiment of the present invention, the steps of acquiring a target pathological image, performing image preprocessing on the target pathological image, and generating a standard pathological image specifically include the following steps:

[0079] Step S1011, acquiring a target pathological image;

[0080] Step S1012, removing noise from the target pathological image to generate a denoised pathological image;

[0081] Step S1013, performing contrast enhancement on the denoised pathological image to generate a contrast pathological image;

[0082] Step S1014: performing standardization processing on the contrast pathology image to generate a standard pathology image.

[0083] Furthermore, the pathological image processing method based on lesion target area selection further includes the following steps:

[0084] Step S102 , performing fuzzy feature analysis on the standard pathological image, determining a suspicious lesion area from the standard pathological image, and extracting a suspicious lesion image.

[0085] In an embodiment of the present invention, fuzzy feature data is obtained by performing an overall texture, color, shape and other fuzzy feature analysis on the standard pathology image. Based on the fuzzy feature data, methods such as sliding window, region growing, and superpixel segmentation are used to locate candidate lesions in the standard pathology image, determine the suspicious lesion area, and then extract the suspicious lesion image corresponding to the suspicious lesion area from the standard pathology image.

[0086] Specifically, Figure 3 A flow chart of extracting suspicious lesion images in the method provided by an embodiment of the present invention is shown.

[0087] In a preferred embodiment of the present invention, the fuzzy feature analysis is performed on the standard pathology image, and the suspicious lesion area is determined from the standard pathology image. The extraction of the suspicious lesion image specifically includes the following steps:

[0088] Step S1021, performing overall fuzzy feature analysis on the standard pathological image to obtain fuzzy feature data;

[0089] Step S1022, based on the fuzzy feature data, locating candidate lesions on the standard pathological image to determine suspicious lesion areas;

[0090] Step S1023 : extracting a suspicious lesion image corresponding to the suspicious lesion area from the standard pathological image.

[0091] Furthermore, performing overall fuzzy feature analysis on the standard pathological image to obtain fuzzy feature data specifically includes the following steps:

[0092] The standard pathological image is blurred using the Gaussian blur kernel to obtain the blurred image. The corresponding process has the following relationship:

[0093] ;

[0094] in, represents a standard pathological image, represents the Gaussian blur kernel, represents the blurred image, represents the image coordinates, Indicates the current offset position on the image. Indicates that the image is at position The pixel value of Indicates The center is The Gaussian kernel is at the offset position The value of

[0095] According to the blurred image, a color histogram is constructed to obtain color features. The corresponding process has the following relationship:

[0096] ;

[0097] in, represents the color histogram, represents the Dirac function, represents an interval of the color histogram, Indicates the total number of pixels in the image, Indicates that the blurred image is at position Pixel value of

[0098] The gray-level co-occurrence matrix is defined using the blurred image. The corresponding process has the following relationship:

[0099] ;

[0100] in, Indicates distance, Indicates angle, Indicates distance and angles The gray-level co-occurrence matrix under Represents two different values of grayscale in an image;

[0101] According to the obtained gray-level co-occurrence matrix, the contrast texture features, homogeneity texture features and energy texture features are calculated respectively. The corresponding processes have the following relationship:

[0102] ;

[0103] ;

[0104] ;

[0105] in, represents contrast texture features, Represents homogeneous texture features, Represents energy texture features;

[0106] Use the Sobel operator to perform edge detection on the blurred image in the horizontal and vertical directions to obtain the edge detection results. The corresponding process has the following relationship:

[0107] ;

[0108] in, represents the edge detection result, Represent the horizontal Sobel operator and the vertical Sobel operator respectively;

[0109] According to the edge detection results, the edge intensity sum, edge density and edge direction histogram are calculated respectively. The corresponding process has the following relationship:

[0110] ;

[0111] ;

[0112] ;

[0113] in, represents the sum of edge strengths, represents the edge density, represents the edge direction histogram, Indicates that the edge detection result is at position The intensity value of Indicates that the edge is at position The direction angle at Indicates the specified direction angle range;

[0114] Combining color histogram, contrast texture features, homogeneity texture features, energy texture features, edge intensity sum, edge density and edge direction histogram, we can get fuzzy feature data. The corresponding process has the following relationship:

[0115] ;

[0116] in, Represents fuzzy feature data.

[0117] Furthermore, based on the fuzzy feature data, lesion candidate positioning is performed on the standard pathological image, and determining the suspicious lesion area specifically includes the following steps:

[0118] Set multiple window sizes and divide the fuzzy feature data according to different window sizes based on the sliding window operation. The corresponding process has the following relationship:

[0119] ;

[0120] in, Indicates the position of the sliding window at the kth scale area, Indicates the coordinates of the upper left corner of the sliding window, Indicates the size of the window;

[0121] The average value of all features in the divided window is taken as the comprehensive feature of the window. The corresponding process has the following relationship:

[0122] ;

[0123] in, Represents the comprehensive characteristics of the window;

[0124] The window comprehensive features of different windows are fused to obtain the multi-scale comprehensive feature vector. The corresponding process has the following relationship:

[0125] ;

[0126] in, represents the multi-scale comprehensive feature vector, Indicates the number of scales, represents the weight of the k-th scale;

[0127] The K-means clustering algorithm is used to classify similar regions in the multi-scale comprehensive feature vector into the same category. The corresponding process has the following relationship:

[0128] ;

[0129] in, represents the set of clusters after clustering, represents the number of clusters, Represents the K-means clustering algorithm;

[0130] Given a fuzzy feature template representing typical lesion features, the matching degree between each cluster and the fuzzy feature template is calculated. The corresponding process has the following relationship:

[0131] ;

[0132] in, Indicates the matching degree, represents the fuzzy feature template, Represents the distance metric operation;

[0133] Set the matching threshold and select the windows contained in the clusters with high matching degree as seed points for region growing. The corresponding process has the following relationship:

[0134] ;

[0135] ;

[0136] in, represents the initial seed point, represents the matching threshold, represents the growth area, represents a growth operation;

[0137] The SLIC algorithm is used to perform superpixel segmentation on the standard pathological image to obtain a superpixel set. The corresponding process has the following relationship:

[0138] ;

[0139] in, represents a set of superpixels, represents the number of superpixels, represents the compactness parameter, represents the SLIC algorithm;

[0140] By merging the superpixel set with the superpixels that intersect the growing region, the candidate region is updated and expanded, and the suspicious lesion region is determined. The corresponding process has the following relationship:

[0141] ;

[0142] in, Indicates suspicious lesion areas. Represents the empty set.

[0143] Furthermore, the pathological image processing method based on lesion target area selection further includes the following steps:

[0144] Step S103 , performing fine segmentation processing on the suspicious lesion image, determining the lesion segmentation boundary, and extracting the target lesion image from the suspicious lesion image.

[0145] In an embodiment of the present invention, the quality of the suspicious lesion image is determined by performing a quality analysis on the suspicious lesion image, and then a target cutting algorithm is selected based on the lesion image quality, which may be Graph Cut, Level Set, U-Net, Mask R-CNN, etc., and then based on the target cutting algorithm, the suspicious lesion image is finely segmented to determine the lesion segmentation boundary, and then the target lesion image corresponding to the lesion segmentation boundary is extracted from the suspicious lesion image.

[0146] Specifically, Figure 4 A flow chart of extracting a target lesion image in a method provided by an embodiment of the present invention is shown.

[0147] In a preferred embodiment of the present invention, the fine segmentation of the suspicious lesion image, determining the lesion segmentation boundary, and extracting the target lesion image from the suspicious lesion image specifically include the following steps:

[0148] Step S1031, performing quality analysis on the suspicious lesion image to determine the lesion image quality;

[0149] Step S1032, selecting a target segmentation algorithm according to the quality of the lesion image;

[0150] Step S1033, performing fine segmentation processing on the suspicious lesion image based on the target segmentation algorithm to determine the lesion segmentation boundary;

[0151] Step S1034: extracting a target lesion image corresponding to the lesion segmentation boundary from the suspicious lesion image.

[0152] Furthermore, extracting the target lesion image corresponding to the lesion segmentation boundary from the suspicious lesion image specifically includes the following steps:

[0153] Generate a binary mask using the finely segmented boundary, multiply the mask by the suspicious lesion image pixel by pixel, and extract the lesion area image containing only the lesion area;

[0154] Adaptive histogram equalization is sequentially applied to the lesion area image to adjust the contrast of the lesion area, Laplace sharpening is applied to enhance the edge clarity and detail expression of the lesion area to obtain an enhanced lesion area image, and color correction is performed on the enhanced lesion area image to obtain a corrected lesion area image;

[0155] The Patch-based texture synthesis method fills the missing parts in the lesion area of the corrected lesion area image to ensure the integrity of the area, and then uses the Gabor filter to enhance the texture features to obtain the target lesion image.

[0156] Furthermore, the pathological image processing method based on lesion target area selection further includes the following steps:

[0157] Step S104 , extracting lesion features from the target lesion image to obtain lesion feature data, and performing lesion classification, grading, and quantitative analysis based on the lesion feature data to obtain a lesion analysis report.

[0158] In an embodiment of the present invention, lesion features such as texture, shape, size, and grayscale distribution are extracted from the target lesion image to obtain lesion feature data. Then, based on the lesion feature data, lesion classification analysis is performed using machine learning algorithms such as support vector machine (SVM) and random forest to obtain lesion classification results. Lesion grading analysis is performed to obtain lesion grading results, and lesion quantification analysis is performed to obtain lesion quantification results. By integrating the lesion classification results, lesion grading results, and lesion quantification results, a lesion analysis report is obtained.

[0159] Specifically, Figure 5 A flow chart of obtaining a lesion analysis report in the method provided in an embodiment of the present invention is shown.

[0160] Among them, in the preferred embodiment provided by the present invention, the lesion feature extraction of the target lesion image, obtaining lesion feature data, performing lesion classification, grading and quantitative analysis based on the lesion feature data, and obtaining a lesion analysis report specifically includes the following steps:

[0161] Step S1041, extracting lesion features from the target lesion image to obtain lesion feature data;

[0162] Step S1042, performing lesion classification analysis based on the lesion feature data to obtain a lesion classification result;

[0163] Step S1043, performing lesion grading analysis based on the lesion characteristic data to obtain a lesion grading result;

[0164] Step S1044, performing lesion quantitative analysis based on the lesion characteristic data to obtain a lesion quantification result;

[0165] Step S1045 , synthesizing the lesion classification result, the lesion grading result, and the lesion quantification result to obtain a lesion analysis report.

[0166] Furthermore, the pathological image processing method based on lesion target area selection further includes the following steps:

[0167] Step S105 : creating a target lesion image and a background lesion image based on the lesion analysis report, synthesizing and visually displaying a multi-dimensional lesion image.

[0168] In an embodiment of the present invention, based on the lesion analysis report, a target lesion image and a background lesion image are created, and then the display position and display ratio of the target lesion image are determined. Then, according to the display position and display ratio, the target lesion image and the background lesion image are multi-dimensionally processed to synthesize a multi-dimensional lesion image. When there is a display requirement, the multi-dimensional lesion image is visualized.

[0169] It can be understood that the target lesion image is a target lesion image combined with part of the lesion analysis report; the background lesion image is a standard pathology image combined with part of the lesion analysis report.

[0170] Specifically, Figure 6 A flow chart of synthesizing a multi-dimensional lesion image in the method provided by an embodiment of the present invention is shown.

[0171] In a preferred embodiment of the present invention, creating a target lesion image and a background lesion image based on the lesion analysis report, synthesizing and visually displaying a multi-dimensional lesion image specifically includes the following steps:

[0172] Step S1051, creating a target lesion image and a background lesion image based on the lesion analysis report;

[0173] Step S1052, determining the display position and display ratio of the target lesion image;

[0174] Step S1053, performing multi-dimensional processing on the target lesion image and the background lesion image according to the display position and the display ratio to synthesize a multi-dimensional lesion image;

[0175] Step S1054: Visually display the multi-dimensional lesion image.

[0176] Further, Figure 7 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0177] Among them, in another preferred embodiment provided by the present invention, a pathology image processing system based on lesion target area selection includes:

[0178] The pathology image preprocessing unit 101 is configured to acquire a target pathology image, perform image preprocessing on the target pathology image, and generate a standard pathology image.

[0179] In an embodiment of the present invention, the pathology image preprocessing unit 101 uses Gaussian filtering and / or median filtering to remove random noise and salt and pepper noise from the target pathology image to generate a denoised pathology image, and then uses methods such as histogram equalization or CLAHE to adjust the contrast of the denoised pathology image to generate a contrast pathology image. Thereafter, the contrast pathology image is standardized in terms of size and resolution to generate a standard pathology image.

[0180] Specifically, Figure 8 FIG. 1 shows a structural block diagram of the pathological image preprocessing unit 101 in the system provided by an embodiment of the present invention.

[0181] In a preferred embodiment of the present invention, the pathological image preprocessing unit 101 specifically includes:

[0182] An image acquisition module 1011 is used to acquire a target pathological image;

[0183] A noise removal module 1012 is configured to remove noise from the target pathological image to generate a denoised pathological image;

[0184] A contrast enhancement module 1013 is configured to perform contrast enhancement on the denoised pathological image to generate a contrast pathological image;

[0185] The standardization processing module 1014 is configured to perform standardization processing on the contrast pathology image to generate a standard pathology image.

[0186] Furthermore, the pathology image processing system based on lesion target area selection further includes:

[0187] The fuzzy feature analysis unit 102 is configured to perform fuzzy feature analysis on the standard pathology image, determine a suspicious lesion area from the standard pathology image, and extract a suspicious lesion image.

[0188] In an embodiment of the present invention, the fuzzy feature analysis unit 102 obtains fuzzy feature data by performing an overall fuzzy feature analysis of the texture, color, shape, etc. of the standard pathology image. Based on the fuzzy feature data, the fuzzy feature analysis unit 102 uses methods such as sliding windows, region growing, and superpixel segmentation to locate candidate lesions in the standard pathology image, determine the suspicious lesion area, and then extract the suspicious lesion image corresponding to the suspicious lesion area from the standard pathology image.

[0189] Specifically, Figure 9FIG. 1 shows a structural block diagram of the fuzzy feature analysis unit 102 in the system provided by an embodiment of the present invention.

[0190] In a preferred embodiment of the present invention, the fuzzy feature analysis unit 102 specifically includes:

[0191] A fuzzy feature analysis module 1021 is configured to perform an overall fuzzy feature analysis on the standard pathological image to obtain fuzzy feature data;

[0192] A lesion candidate positioning module 1022 is configured to perform lesion candidate positioning on the standard pathology image based on the fuzzy feature data to determine a suspicious lesion area;

[0193] The suspicious lesion image extraction module 1023 is configured to extract a suspicious lesion image corresponding to a suspicious lesion region from the standard pathological image.

[0194] Furthermore, the pathology image processing system based on lesion target area selection further includes:

[0195] The fine segmentation processing unit 103 is configured to perform fine segmentation processing on the suspicious lesion image, determine the lesion segmentation boundary, and extract the target lesion image from the suspicious lesion image.

[0196] In an embodiment of the present invention, the fine segmentation processing unit 103 determines the quality of the lesion image by performing a quality analysis on the suspicious lesion image, and then selects a target cutting algorithm based on the lesion image quality, which may be Graph Cut, Level Set, U-Net, Mask R-CNN, etc., and then performs fine segmentation processing on the suspicious lesion image based on the target cutting algorithm to determine the lesion segmentation boundary, and then extracts the target lesion image corresponding to the lesion segmentation boundary from the suspicious lesion image.

[0197] Specifically, Figure 10 FIG. 1 shows a structural block diagram of the fine segmentation processing unit 103 in the system provided by an embodiment of the present invention.

[0198] In a preferred embodiment of the present invention, the fine segmentation processing unit 103 specifically includes:

[0199] The quality analysis module 1031 is used to perform quality analysis on the suspicious lesion image to determine the quality of the lesion image;

[0200] A cutting algorithm selection module 1032 is configured to select a target cutting algorithm based on the quality of the lesion image;

[0201] A fine segmentation processing module 1033 is configured to perform fine segmentation processing on the suspicious lesion image based on the target segmentation algorithm to determine the lesion segmentation boundary;

[0202] The target lesion image extraction module 1034 is used to extract the target lesion image corresponding to the lesion segmentation boundary from the suspicious lesion image.

[0203] Furthermore, the pathology image processing system based on lesion target area selection further includes:

[0204] The lesion feature extraction unit 104 is used to extract lesion features from the target lesion image, obtain lesion feature data, and perform lesion classification, grading and quantitative analysis based on the lesion feature data to obtain a lesion analysis report.

[0205] In an embodiment of the present invention, the lesion feature extraction unit 104 extracts lesion features such as texture, shape, size, and grayscale distribution from the target lesion image to obtain lesion feature data, and then performs lesion classification analysis based on the lesion feature data using machine learning algorithms such as support vector machine (SVM) and random forest to obtain lesion classification results, performs lesion grading analysis to obtain lesion grading results, and performs lesion quantification analysis to obtain lesion quantification results. A lesion analysis report is obtained by integrating the lesion classification results, lesion grading results, and lesion quantification results.

[0206] The multi-dimensional visualization display unit 105 is used to create a target lesion image and a background lesion image based on the lesion analysis report, and synthesize and visualize the multi-dimensional lesion image.

[0207] In an embodiment of the present invention, the multidimensional visualization display unit 105 creates a target lesion image and a background lesion image based on the lesion analysis report, and then determines the display position and display ratio of the target lesion image. Then, according to the display position and display ratio, the target lesion image and the background lesion image are multidimensionally processed to synthesize a multidimensional lesion image. When there is a display requirement, the multidimensional visualization display unit 105 visualizes the multidimensional lesion image.

[0208] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0209] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. Any reference to memory, storage, database, or other media used in the various embodiments provided herein may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM).

[0210] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0211] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0212] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A pathological image processing method based on lesion target area selection, characterized in that: The method specifically comprises the following steps: Acquiring a target pathological image, performing image preprocessing on the target pathological image, and generating a standard pathological image; Performing fuzzy feature analysis on the standard pathological image, determining a suspicious lesion area from the standard pathological image, and extracting a suspicious lesion image; Performing fine segmentation processing on the suspicious lesion image, determining the lesion segmentation boundary, and extracting the target lesion image from the suspicious lesion image; Extracting lesion features from the target lesion image to obtain lesion feature data, and performing lesion classification, grading, and quantitative analysis based on the lesion feature data to obtain a lesion analysis report; Based on the lesion analysis report, creating a target lesion image and a background lesion image, synthesizing and visually displaying a multi-dimensional lesion image; The fuzzy feature analysis is performed on the standard pathological image to determine the suspicious lesion area from the standard pathological image, and the extraction of the suspicious lesion image specifically includes the following steps: Performing overall fuzzy feature analysis on the standard pathological image to obtain fuzzy feature data; Based on the fuzzy feature data, locating candidate lesions on the standard pathological image to determine suspicious lesion areas; Extracting a suspicious lesion image corresponding to a suspicious lesion area from the standard pathological image; Performing overall fuzzy feature analysis on the standard pathological image to obtain fuzzy feature data specifically includes the following steps: The standard pathological image is blurred using the Gaussian blur kernel to obtain the blurred image. The corresponding process has the following relationship: ； in, represents a standard pathological image, represents the Gaussian blur kernel, represents the blurred image, represents the image coordinates, Indicates the current offset position on the image. Indicates that the image is at position The pixel value of Indicates The center is The Gaussian kernel is at the offset position The value of According to the blurred image, a color histogram is constructed to obtain color features. The corresponding process has the following relationship: ； in, represents the color histogram, represents the Dirac function, represents an interval of the color histogram, Indicates the total number of pixels in the image, Indicates that the blurred image is at position Pixel value of The gray-level co-occurrence matrix is defined using the blurred image. The corresponding process has the following relationship: ； in, Indicates distance, Indicates angle, Indicates distance and angles The gray-level co-occurrence matrix under Represents two different values of grayscale in an image; According to the obtained gray-level co-occurrence matrix, the contrast texture features, homogeneity texture features and energy texture features are calculated respectively. The corresponding processes have the following relationship: ； ； ； in, represents contrast texture features, Represents homogeneous texture features, Represents energy texture features; Use the Sobel operator to perform edge detection on the blurred image in the horizontal and vertical directions to obtain the edge detection results. The corresponding process has the following relationship: ； in, represents the edge detection result, Represent the horizontal Sobel operator and the vertical Sobel operator respectively; According to the edge detection results, the edge intensity sum, edge density and edge direction histogram are calculated respectively. The corresponding process has the following relationship: ； ； ； in, represents the sum of edge strengths, represents the edge density, represents the edge direction histogram, Indicates that the edge detection result is at position The intensity value of Indicates that the edge is at position The direction angle at Indicates the specified direction angle range; Combining color histogram, contrast texture features, homogeneity texture features, energy texture features, edge intensity sum, edge density and edge direction histogram, we can get fuzzy feature data. The corresponding process has the following relationship: ； in, Represents fuzzy feature data; Based on the fuzzy feature data, locating candidate lesions on the standard pathological image and determining suspicious lesion areas specifically include the following steps: Set multiple window sizes and divide the fuzzy feature data according to different window sizes based on the sliding window operation. The corresponding process has the following relationship: ； in, Indicates the position of the sliding window at the kth scale area, Indicates the coordinates of the upper left corner of the sliding window, Indicates the size of the window; The average value of all features in the divided window is taken as the comprehensive feature of the window. The corresponding process has the following relationship: ； in, Represents the comprehensive characteristics of the window; The window comprehensive features of different windows are fused to obtain the multi-scale comprehensive feature vector. The corresponding process has the following relationship: ； in, represents the multi-scale comprehensive feature vector, Indicates the number of scales, represents the weight of the k-th scale; The K-means clustering algorithm is used to classify similar regions in the multi-scale comprehensive feature vector into the same category. The corresponding process has the following relationship: ； in, represents the set of clusters after clustering, represents the number of clusters, Represents the K-means clustering algorithm; Given a fuzzy feature template representing typical lesion features, the matching degree between each cluster and the fuzzy feature template is calculated. The corresponding process has the following relationship: ； in, Indicates the matching degree, represents the fuzzy feature template, Represents the distance metric operation; Set the matching threshold and select the windows contained in the clusters with high matching degree as seed points for region growing. The corresponding process has the following relationship: ； ； in, represents the initial seed point, represents the matching threshold, represents the growth area, represents a growth operation; The SLIC algorithm is used to perform superpixel segmentation on the standard pathological image to obtain a superpixel set. The corresponding process has the following relationship: ； in, represents a superpixel set, represents the number of superpixels, represents the compactness parameter, represents the SLIC algorithm; By merging the superpixel set with the superpixels that intersect with the growing region, the candidate region is updated and expanded, and the suspicious lesion region is determined. The corresponding process has the following relationship: ； in, Indicates suspicious lesion areas. Represents the empty set.

2. The pathological image processing method based on lesion target area selection according to claim 1, characterized in that: The acquiring of the target pathological image, performing image preprocessing on the target pathological image, and generating a standard pathological image specifically include the following steps: Acquire target pathological images; performing noise removal on the target pathological image to generate a denoised pathological image; performing contrast enhancement on the denoised pathological image to generate a contrast pathological image; The contrast pathology image is standardized to generate a standard pathology image.

3. The pathological image processing method based on lesion target area selection according to claim 1, characterized in that: The fine segmentation processing of the suspicious lesion image, determining the lesion segmentation boundary, and extracting the target lesion image from the suspicious lesion image specifically includes the following steps: Performing quality analysis on the suspicious lesion image to determine the lesion image quality; Selecting a target cutting algorithm according to the quality of the lesion image; Based on the target cutting algorithm, fine segmentation processing is performed on the suspicious lesion image to determine the lesion segmentation boundary; From the suspicious lesion image, the target lesion image corresponding to the lesion segmentation boundary is extracted.

4. The pathological image processing method based on lesion target area selection according to claim 3, characterized in that: Extracting the target lesion image corresponding to the lesion segmentation boundary from the suspicious lesion image specifically includes the following steps: Generate a binary mask using the finely segmented boundary, multiply the mask by the suspicious lesion image pixel by pixel, and extract the lesion area image containing only the lesion area; Adaptive histogram equalization is sequentially applied to the lesion area image to adjust the contrast of the lesion area, Laplace sharpening is applied to enhance the edge clarity and detail expression of the lesion area to obtain an enhanced lesion area image, and color correction is performed on the enhanced lesion area image to obtain a corrected lesion area image; The Patch-based texture synthesis method fills the missing parts in the lesion area of the corrected lesion area image to ensure the integrity of the area, and then uses the Gabor filter to enhance the texture features to obtain the target lesion image.

5. The pathological image processing method based on lesion target area selection according to claim 4, characterized in that: The extracting of lesion features from the target lesion image to obtain lesion feature data, and performing lesion classification, grading, and quantitative analysis based on the lesion feature data to obtain a lesion analysis report specifically includes the following steps: Extracting lesion features from the target lesion image to obtain lesion feature data; Performing lesion classification analysis based on the lesion characteristic data to obtain a lesion classification result; Performing lesion grading analysis based on the lesion characteristic data to obtain a lesion grading result; Performing lesion quantitative analysis based on the lesion characteristic data to obtain lesion quantification results; The lesion classification result, the lesion grading result and the lesion quantification result are combined to obtain a lesion analysis report.

6. The pathological image processing method based on lesion target area selection according to claim 5, characterized in that: The step of creating a target lesion image and a background lesion image based on the lesion analysis report, and synthesizing and visually displaying a multi-dimensional lesion image specifically includes the following steps: Creating a target lesion image and a background lesion image based on the lesion analysis report; Determining a display position and display ratio of the target lesion image; performing multi-dimensional processing on the target lesion image and the background lesion image according to the display position and the display ratio to synthesize a multi-dimensional lesion image; The multi-dimensional lesion image is visually displayed.

7. A pathology image processing system based on lesion target area selection, the system applying the pathology image processing method based on lesion target area selection according to any one of claims 1 to 6, characterized in that: The system includes a pathological image preprocessing unit, a fuzzy feature analysis unit, a fine segmentation processing unit, a lesion feature extraction unit and a multi-dimensional visualization display unit, wherein: A pathology image preprocessing unit, configured to acquire a target pathology image, perform image preprocessing on the target pathology image, and generate a standard pathology image; a fuzzy feature analysis unit, configured to perform fuzzy feature analysis on the standard pathology image, determine a suspicious lesion area from the standard pathology image, and extract a suspicious lesion image; a fine segmentation processing unit, configured to perform fine segmentation processing on the suspicious lesion image, determine the lesion segmentation boundary, and extract the target lesion image from the suspicious lesion image; a lesion feature extraction unit, configured to extract lesion features from the target lesion image, obtain lesion feature data, and perform lesion classification, grading, and quantitative analysis based on the lesion feature data to obtain a lesion analysis report; The multi-dimensional visualization display unit is used to create a target lesion image and a background lesion image based on the lesion analysis report, and synthesize and visualize the multi-dimensional lesion image.

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