A method and system for analyzing nervous system images

Abstract

The present invention relates to the field of image analysis technology, and discloses a method and system for analyzing nervous system images, comprising a data acquisition unit, an image processing unit, a segmentation processing unit, a feature extraction unit, a sample database, and an analysis and evaluation unit. First, the image processing unit converts the image into a grayscale image and performs normalization processing. Then, the segmentation processing unit performs segmentation based on the normalized grayscale value to obtain multiple pixel domains. The feature extraction unit extracts pixel domain features. The sample database stores pre-trained sample pixel domains and feature data, and adds diagnostic result annotations. During diagnostic analysis, the similarity between the pixel domain of the image to be analyzed and the sample pixel domain is calculated, and the sample pixel domain that meets the conditions is screened out to determine the diagnostic result. If there is no comparable sample, the diagnosis result is determined based on the experience of relevant personnel and stored in the database. The present invention can accurately analyze images, comprehensively extract features, and use the sample database to assist in diagnosis, thereby improving efficiency and accuracy.

Description

Technical Field

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

[0002] Neurological diseases are a serious health threat, and early and accurate diagnosis is crucial for treatment and prognosis. Currently, neurological imaging analysis plays a key role in the diagnosis of neurological diseases. However, existing neurological imaging analysis methods have some shortcomings.

[0003] Traditional neurological imaging analysis relies primarily on subjective judgment based on physician experience. This approach is not only inefficient but also susceptible to subjective factors, making it difficult to ensure the accuracy and consistency of diagnostic results. Furthermore, with the continuous advancement of medical imaging technology, the amount of neurological imaging data continues to increase, and traditional manual analysis methods can no longer meet the demand for rapid and accurate diagnosis. To improve the accuracy and efficiency of neurological imaging analysis, a more objective and accurate analysis method is needed. In recent years, with the continuous development of computer technology and image processing technology, computer-based neurological imaging analysis methods have gradually become a research hotspot.

[0004] To address the challenges of existing neurological imaging analysis methods, the present invention proposes a neurological imaging analysis method and system. This method and system improves the accuracy and efficiency of neurological imaging analysis by automatically segmenting, extracting features, and performing diagnostic analysis on neurological images, providing strong support for the early diagnosis and treatment of neurological diseases. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for analyzing nervous system images, which solve the technical problems raised in the background technology.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] A method and system for analyzing nervous system images, comprising:

[0008] a data acquisition unit, for acquiring nervous system images to be analyzed;

[0009] An image processing unit, configured to convert the nervous system image into a grayscale image, and then perform image normalization processing on the grayscale value of each pixel on the grayscale image to obtain the normalized grayscale value of each pixel;

[0010] a segmentation processing unit for segmenting the nervous system image based on the normalized grayscale values ​​of all pixels corresponding to the nervous system image, obtaining a segmentation identifier corresponding to each pixel, and then obtaining a plurality of pixel domains from the nervous system image based on the segmentation identifier, wherein a pixel domain refers to an image block composed of pixels having the same segmentation identifier;

[0011] Segmentation marks are specific labels assigned to pixels to distinguish different pixel domains during the neural system image segmentation process;

[0012] A feature extraction unit is used to perform feature extraction processing on all pixel domains in the nervous system image and obtain pixel domain feature data;

[0013] The pixel domain feature data includes: the area, perimeter and shape factor of the pixel domain;

[0014] A sample database is used to store pre-trained sample pixel domains and their corresponding sample feature data, wherein the sample pixel domains are annotated with diagnostic results;

[0015] The analysis and evaluation unit is used to perform diagnostic analysis on each pixel domain and its pixel domain feature data corresponding to the nervous system image to be analyzed and the sample pixel domain and sample feature data in the sample database, and determine the diagnostic result of the pixel domain corresponding to the nervous system image to be analyzed.

[0016] As a further solution of the present invention: the image normalization processing method is as follows:

[0017] Step A1: Get the grayscale value of each pixel in the grayscale image and mark the grayscale value of each pixel as G in the coordinate system. (i,j) , where (i, j) represents the pixel coordinates in the image, i represents the nth horizontal coordinate, j represents the nth vertical coordinate, i = 1, 2, ... n, j = 1, 2, ... m, and n × m is the number of all pixels in the nervous system image;

[0018] Where i = 1, 2, ... n, n represents the number of all pixels;

[0019] Step A2: Get the grayscale value with the largest value and the grayscale value with the smallest value from the grayscale values ​​of all pixels, and mark them as G and G respectively. max and Gmin ;

[0020] Step A3, through H (i,j) =(G (i,j) -G min ) / (G max -G min ), and obtain the gray value H of each pixel after normalization (i,j).

[0021] As a further solution of the present invention: the segmentation processing method is as follows:

[0022] Step B1: Select a pixel from the nervous system image as a reference pixel and obtain the grayscale value H corresponding to the reference pixel. (i,j) ;

[0023] At the same time, add segmentation marks for the benchmark pixels;

[0024] Step B2, based on H (i,j) , obtain the grayscale values ​​H of multiple pixels adjacent to the reference pixel (i-1,j) 、H (i+1,j) 、H (i,j-1) 、H (i,j+1) 、H (i+1,j+1) 、H (i+1,j-1) 、H (i-1,j-1) 、H (i-1,j+1) ;

[0025] Step B3, calculate the gray value H corresponding to the reference pixel points respectively (i,j) The grayscale values ​​H of the adjacent pixels are (i-1,j) 、H (i+1,j) 、H (i,j-1) 、H (i,j+1) 、H (i+1,j+1) 、H (i+1,j-1) 、H (i-1,j-1) 、H (i-1,j+1) The absolute value of the difference between them is then compared with the preset grayscale difference threshold Hy:

[0026] H (i,j) and H (i+1,j) For example;

[0027] If |H (i,j) -H (i+1,j) |>Hy, then a segmentation marker different from the reference pixel point is added to the pixel point adjacent to the reference pixel point;

[0028] If |H (i,j) -H (i+1,j) |≤Hy, then the pixel point adjacent to the reference pixel point will be added with the same segmentation mark as the reference pixel point;

[0029] Step B4: Using each adjacent pixel as a reference pixel in the same manner as Step B3, and obtaining segmentation identifiers of each pixel adjacent to the reference pixel;

[0030] And so on, until all pixels are added with segmentation marks;

[0031] Step B5: Based on the segmentation identifier, all pixels with the same segmentation identifier are grouped into a pixel domain. The pixel domain refers to an image block composed of pixels with the same segmentation identifier.

[0032] As a further solution of the present invention: in Step B4, the pixel points that have been added with segmentation marks are no longer added with segmentation marks again. When adjacent pixel points are used as reference pixel points, the reference pixel points selected in the early stage are no longer added with segmentation marks.

[0033] As a further solution of the present invention: the feature extraction processing method is as follows:

[0034] Select a pixel region;

[0035] Step C1, obtain the number of all pixels in the pixel domain, and then record the number as the area of ​​the pixel domain;

[0036] Step C2, obtaining the number of all pixel points on the boundary of the pixel domain, and then recording the number as the perimeter of the pixel domain;

[0037] The method for determining whether a pixel point in the pixel domain is on the boundary is as follows:

[0038] If at least one of the multiple adjacent pixels of a pixel is not in the pixel domain, the pixel is determined to be on the boundary.

[0039] If all adjacent pixels are in the pixel domain, it is determined that the pixel is not on the boundary;

[0040] Step C3, calculate the shape factor of the pixel domain based on the area and perimeter of the pixel domain;

[0041] Calculation formula: X = 4π × S / C2

[0042] X represents the shape factor of the pixel domain, S represents the area of ​​the pixel domain, and C represents the perimeter of the pixel domain;

[0043] The shape factor is used to measure how close the shape of a pixel domain is to a circular shape. When the shape factor is closer to 1, the pixel domain is closer to a circle. When the difference between the shape factor and 1 is greater, the shape of the pixel domain is more irregular.

[0044] As a further solution of the present invention, the pre-training method of the sample pixel domain and the sample feature data is to collect a large amount of historical nervous system imaging data containing diagnostic results in advance, and obtain all pixel domains and their corresponding pixel domain feature data from the historical nervous system imaging data through the image processing unit, the segmentation processing unit, and the feature extraction unit, and the obtained pixel domains and their corresponding pixel domain feature data are respectively the sample pixel domain and the sample feature data;

[0045] At the same time, diagnostic result labels are added to each pixel domain based on the diagnostic results corresponding to the historical data of nervous system imaging; among them, the diagnostic result labels are added manually.

[0046] As a further solution of the present invention: wherein, the historical data of nervous system images include normal images and images of different types of nervous system diseases.

[0047] As a further solution of the present invention: the diagnostic analysis method is as follows:

[0048] Step D1, select a pixel domain and its pixel domain feature data corresponding to a neural system image to be analyzed, and a sample pixel domain and its corresponding sample feature data as an example;

[0049] Then, the shape factor, area, and perimeter of the pixel domain feature data corresponding to the nervous system image to be analyzed are obtained and marked as X0, S0, and C0 respectively;

[0050] Step D2: Mark the shape factor, area, and perimeter of the sample feature data corresponding to a sample pixel domain as X. y 、S y and C y ;

[0051] Step D3, then pass

[0052] K=1 / (|X0-X y |×β1+|S0-S y |×β2+|C0-C y |×β3)

[0053] Where β1, β2, and β3 are the corresponding preset weight coefficients respectively;

[0054] Calculating the similarity K between the pixel domain corresponding to the nervous system image to be analyzed and the sample pixel domain;

[0055] Step D4, calculating the similarity between the pixel domain corresponding to the nervous system image to be analyzed and the pixel domains of all samples according to the method of Step D1 to Step D3;

[0056] Step D5. Compare each obtained similarity with a preset similarity threshold, and based on the comparison result, obtain the corresponding sample pixel domain when the similarity is greater than the similarity threshold, then obtain a sample pixel domain with the largest similarity value, and then obtain the diagnostic result annotation of the sample pixel domain, and use the diagnostic result annotation of the sample pixel domain as the diagnostic result of the corresponding pixel domain of the nervous system image to be analyzed.

[0057] As a further solution of the present invention: before Step D4, X0 and X y The absolute value of the difference between y The absolute value of the difference between C0 and S0, y The absolute value of the difference between them is used to screen out the sample pixel domain involved in the similarity calculation:

[0058] Select a sample pixel domain;

[0059] If |X0-X y |>XL,|S0-S y |>SL,|C0-C y |>If any of the items in CL is true, the sample pixel domain will not be extracted to participate in the similarity calculation.

[0060] If |X0-X y |>XL,|S0-S y |>SL,|C0-C y |>CL does not hold, then the sample pixel domain is extracted to participate in the similarity calculation;

[0061] Among them, XL, SL, and CL are the corresponding preset difference screening thresholds;

[0062] As a further solution of the present invention: in Step D4, when all similarities are less than or equal to the similarity threshold, it means that the sample pixel domain does not contain a similar sample pixel domain in the sample database, and then the diagnosis result is determined by relevant personnel based on their experience;

[0063] At the same time, the sample pixel domain and its corresponding pixel domain feature data are stored in the sample database as the sample pixel domain and sample feature data.

[0064] A method for analyzing nervous system images is implemented by the nervous system image analysis system, and the method comprises the following steps:

[0065] Step 1: Data Acquisition

[0066] Obtaining images of the nervous system to be analyzed;

[0067] Step 2: Image processing

[0068] Convert the nervous system image into a grayscale image, then perform image normalization on the grayscale value of each pixel on the grayscale image, and obtain the normalized grayscale value of each pixel;

[0069] Step 3: Segmentation

[0070] According to the normalized grayscale values ​​of all pixels corresponding to the nervous system image, the nervous system image is segmented and a segmentation mark corresponding to each pixel is obtained, and then multiple pixel domains are obtained from the nervous system image according to the segmentation mark;

[0071] Step 4: Feature extraction

[0072] Perform feature extraction on all pixel domains in the nervous system image and obtain pixel domain feature data;

[0073] Step 5: Sample database preparation

[0074] A large amount of historical neurological imaging data containing diagnostic results is collected in advance, and all pixel domains and their corresponding pixel domain feature data are obtained from the historical neurological imaging data through image processing, segmentation processing, and feature extraction. The obtained pixel domains and their corresponding pixel domain feature data are respectively the sample pixel domains and sample feature data. At the same time, the diagnostic results obtained in advance from the historical neurological imaging data are used to add diagnostic result labels to each pixel domain.

[0075] Step 6: Analysis and Evaluation

[0076] The pixel domains and their pixel domain feature data corresponding to the nervous system image to be analyzed are diagnosed and analyzed with the sample pixel domains and sample feature data in the sample database, and the diagnosis result of the pixel domain corresponding to the nervous system image to be analyzed is determined.

[0077] Beneficial effects of the present invention:

[0078] The present invention converts nervous system images into grayscale images and performs normalization processing, thereby providing a standardized data basis for subsequent analysis, reducing interference caused by factors such as image brightness differences, and making the analysis results more accurate and reliable.

[0079] The present invention performs image segmentation processing, assigns segmentation labels to pixels based on normalized grayscale values, and then obtains multiple pixel domains. This segmentation method can divide complex nervous system images into different regions, facilitating a more detailed analysis of the characteristics of each region.

[0080] In the present invention, the feature extraction unit extracts features from the pixel domain from three aspects: area, perimeter and shape factor. The area and perimeter can intuitively reflect the size and boundary conditions of the pixel domain, while the shape factor can measure the degree of proximity of the pixel domain to a circle, thereby more comprehensively describing the morphological characteristics of the pixel domain.

[0081] The present invention uses a large amount of pre-collected historical data of nervous system images containing diagnostic results for pre-training to obtain sample pixel domains and their corresponding sample feature data, and stores them in a sample database. These sample data cover normal images and images of different types of nervous system diseases, providing a rich reference basis for diagnostic analysis.

[0082] In the present invention, the sample pixel domain in the sample database is annotated with the diagnostic result. By comparing and analyzing the pixel domain feature data of the image to be analyzed with the data in the sample database, the diagnostic result of the pixel domain to be analyzed can be quickly determined, thereby improving the diagnostic efficiency.

[0083] Before calculating similarity, the present invention screens out sample pixel domains for similarity calculation by comparing the absolute differences in shape factor, area, and perimeter between the pixel domain to be analyzed and the sample pixel domain with a preset difference screening threshold. This avoids unnecessary calculations for sample pixel domains with significant differences, reduces computational effort, and improves efficiency.

[0084] In this invention, when all similarities are less than or equal to the similarity threshold, it indicates that there are no similar sample pixel domains in the sample database. At this point, the diagnosis result is determined by relevant personnel based on their experience, and the sample pixel domain and its feature data are stored in the sample database. This not only provides a solution for current diagnosis, but also continuously enriches the sample database, improves the adaptability and accuracy of the system, and provides more references for subsequent diagnostic analysis.

[0085] In summary, the nervous system image analysis method and system of the present invention can achieve accurate analysis and diagnosis of nervous system images, improve diagnostic efficiency and accuracy, and have good scalability and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The present invention will be further described below with reference to the accompanying drawings.

[0087] Figure 1 This is a system block diagram of a nervous system image analysis system of the present invention.

[0088] Figure 2 It is a flow chart of a nervous system image analysis method of the present invention. DETAILED DESCRIPTION

[0089] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0090] Example 1

[0091] See also Figure 1 As shown, the present invention is a method and system for analyzing nervous system images, comprising:

[0092] a data acquisition unit, for acquiring nervous system images to be analyzed;

[0093] An image processing unit, configured to convert the nervous system image into a grayscale image and then perform image normalization on the grayscale value of each pixel in the grayscale image;

[0094] The method is as follows:

[0095] Step A1: Get the grayscale value of each pixel in the grayscale image and mark the grayscale value of each pixel as G in the coordinate system. (i,j) , where (i, j) represents the pixel coordinates in the image, i represents the nth horizontal coordinate, j represents the nth vertical coordinate, i = 1, 2, ... n, j = 1, 2, ... m, and n × m is the number of all pixels in the nervous system image;

[0096] Where i = 1, 2, ... n, n represents the number of all pixels;

[0097] Step A2: Get the grayscale value with the largest value and the grayscale value with the smallest value from the grayscale values ​​of all pixels, and mark them as G and G respectively. max and Gmin ;

[0098] Step A3, through H (i,j) =(G (i,j) -G min ) / (G max -G min ), and obtain the gray value H of each pixel after normalization (i,j) ;

[0099] A segmentation processing unit is used to segment the nervous system image according to the grayscale values ​​of all pixels after normalization;

[0100] The specific method is as follows:

[0101] Step B1: Select a pixel from the nervous system image as a reference pixel and obtain the grayscale value H corresponding to the reference pixel. (i,j) ;

[0102] At the same time, add segmentation marks for the benchmark pixels;

[0103] Step B2, based on H (i,j) , obtain the grayscale values ​​H of multiple pixels adjacent to the reference pixel (i-1,j) 、H (i+1,j) 、H (i,j-1) 、H (i,j+1) 、H (i+1,j+1) 、H (i+1,j-1) 、H (i-1,j-1) 、H (i-1,j+1) ;

[0104] Step B3, calculate the gray value H corresponding to the reference pixel points respectively (i,j) The grayscale values ​​H of the adjacent pixels are (i-1,j) 、H (i+1,j) 、H (i,j-1) 、H (i,j+1) 、H (i+1,j+1) 、H (i+1,j-1) 、H (i-1,j-1) 、H (i-1,j+1) The absolute value of the difference between them is then compared with the preset grayscale difference threshold Hy:

[0105] H (i,j) and H (i+1,j) For example;

[0106] If |H (i,j) -H (i+1,j) |>Hy, then a segmentation marker different from the reference pixel point is added to the pixel point adjacent to the reference pixel point;

[0107] If |H (i,j) -H (i+1,j) |≤Hy, then the pixel point adjacent to the reference pixel point will be added with the same segmentation mark as the reference pixel point;

[0108] Step B4: Using each adjacent pixel as a reference pixel in the same manner as Step B3, and obtaining segmentation identifiers of each pixel adjacent to the reference pixel;

[0109] And so on, until all pixels are added with segmentation marks;

[0110] Among them, the pixel points that have been added with the segmentation mark will not be added again. For example, when the adjacent pixel points are used as the reference pixel points, the reference pixel points selected in the early stage will not be added with the segmentation mark;

[0111] Step B5, based on the segmentation mark, all pixels with the same segmentation mark are grouped into a pixel domain, where the pixel domain refers to an image block composed of pixels with the same segmentation mark;

[0112] A feature extraction unit is used to perform feature extraction processing on all pixel domains in the nervous system image and obtain pixel domain feature data;

[0113] The pixel domain feature data includes: the area, perimeter and shape factor of the pixel domain;

[0114] The specific method is as follows:

[0115] Take a pixel domain as an example;

[0116] Step C1, obtain the number of all pixels in the pixel domain, and then record the number as the area of ​​the pixel domain;

[0117] Step C2, obtaining the number of all pixel points on the boundary of the pixel domain, and then recording the number as the perimeter of the pixel domain;

[0118] The method for determining whether a pixel point in the pixel domain is on the boundary is as follows:

[0119] If at least one of the multiple adjacent pixels of a pixel is not in the pixel domain, the pixel is determined to be on the boundary.

[0120] If all adjacent pixels are in the pixel domain, it is determined that the pixel is not on the boundary;

[0121] Step C3, calculate the shape factor of the pixel domain based on the area and perimeter of the pixel domain;

[0122] Calculation formula: X = 4π × S / C2

[0123] X represents the shape factor of the pixel domain, S represents the area of ​​the pixel domain, and C represents the perimeter of the pixel domain;

[0124] The shape factor is used to measure how close the shape of a pixel domain is to a circular shape. The closer the shape factor is to 1, the closer the pixel domain is to a circle. The greater the difference between the shape factor and 1, the more irregular the shape of the pixel domain.

[0125] A sample database is used to store pre-trained sample pixel domains and their corresponding sample feature data, wherein the sample pixel domains are annotated with diagnostic results;

[0126] The pre-training method of the sample pixel domain and the sample feature data is to collect a large amount of historical neurological imaging data containing diagnostic results in advance, and obtain all pixel domains and their corresponding pixel domain feature data from the historical neurological imaging data through the image processing unit, the segmentation processing unit, and the feature extraction unit, and the obtained pixel domain and its corresponding pixel domain feature data are the sample pixel domain and the sample feature data respectively;

[0127] At the same time, diagnostic result annotations are added to each pixel domain using the diagnostic results corresponding to the historical data of nervous system imaging; wherein, the diagnostic results corresponding to the historical data of nervous system imaging are known results obtained through experience; and the diagnostic result annotations are added manually;

[0128] Among them, the historical data of nervous system imaging include normal images and images of different types of nervous system diseases;

[0129] An analysis and evaluation unit, configured to perform diagnostic analysis on each pixel domain and its pixel domain feature data corresponding to the nervous system image to be analyzed and the sample pixel domain and sample feature data in the sample database;

[0130] The specific method is as follows:

[0131] Step D1, taking a pixel domain and its pixel domain feature data corresponding to a neural system image to be analyzed, and a sample pixel domain and its corresponding sample feature data as an example;

[0132] Then, the shape factor, area, and perimeter of the pixel domain feature data corresponding to the nervous system image to be analyzed are obtained and marked as X0, S0, and C0 respectively;

[0133] Step D2: Mark the shape factor, area, and perimeter of the sample feature data corresponding to a sample pixel domain as X. y 、S y and C y ;

[0134] Step D3, then pass

[0135] K=1 / (|X0-X y |×β1+|S0-S y |×β2+|C0-C y |×β3)

[0136] Calculating the similarity K between the pixel domain corresponding to the nervous system image to be analyzed and the sample pixel domain;

[0137] Where β1, β2, and β3 are the corresponding preset weight coefficients respectively;

[0138] Step D4, calculating the similarity between the pixel domain corresponding to the nervous system image to be analyzed and the pixel domains of all samples according to the method of Step D1 to Step D3;

[0139] Step D5, comparing each obtained similarity with a preset similarity threshold, and based on the comparison result, obtaining the sample pixel domain corresponding to the pixel domain whose similarity is greater than the similarity threshold, then obtaining a sample pixel domain with the largest similarity value, and then obtaining a diagnosis result annotation for the sample pixel domain, and using the diagnosis result annotation of the sample pixel domain as the diagnosis result of the pixel domain corresponding to the nervous system image to be analyzed;

[0140] This embodiment enables automated analysis and diagnosis of nervous system images by acquiring them and performing grayscale conversion, normalization, segmentation, feature extraction, and diagnostic analysis with a sample database. Accurately calculating pixel-domain feature data and comparing it with sample pixel domains to derive diagnostic results helps improve diagnostic accuracy and efficiency and reduce human error. It also provides a foundational technical framework and analytical approach for subsequent embodiments.

[0141] For example, in medical imaging diagnosis, it can quickly and accurately make preliminary judgments on neurological diseases, provide valuable reference for doctors, and save diagnosis time.

[0142] Example 2

[0143] As the second embodiment of the present invention, when this application is implemented, compared with the first embodiment, the technical solution of this embodiment differs from that of the first embodiment only in that:

[0144] This embodiment also uses X0 and X before Step D4 of the first embodiment. y The absolute value of the difference between y The absolute value of the difference between C0 and S0, y The absolute value of the difference between them is used to screen out the sample pixel domain involved in the similarity calculation:

[0145] Select a sample pixel domain;

[0146] If |X0-X y |>XL,|S0-S y |>SL,|C0-C y |>If any of the items in CL is true, the sample pixel domain will not be extracted to participate in the similarity calculation.

[0147] If |X0-X y |>XL,|S0-S y |>SL,|C0-C y|>CL does not hold, then the sample pixel domain is extracted to participate in the similarity calculation;

[0148] Among them, XL, SL, and CL are the corresponding preset difference screening thresholds, which are used to determine whether the corresponding sample pixel domain participates in the similarity calculation;

[0149] Based on the first embodiment, this embodiment adds a step of screening out the sample pixel domain participating in the similarity calculation by using the absolute value of the difference, which can reduce unnecessary calculation amount and improve the efficiency of diagnosis and analysis.

[0150] For example, when faced with a large number of sample pixel domains, it can quickly exclude samples with obvious differences and focus on calculating and analyzing samples that are more likely to be similar.

[0151] Example 3

[0152] As the third embodiment of the present invention, when the present application is specifically implemented, compared with the first and second embodiments, the technical solution of this embodiment is to combine the solutions of the first and second embodiments. The difference between the technical solution of this embodiment and the first and second embodiments is only in this embodiment;

[0153] In this embodiment, in Step D4 of the first embodiment, when all similarities are less than or equal to the similarity threshold, it means that the sample pixel domain does not contain a similar sample pixel domain in the sample database, and then the diagnosis result is determined by relevant personnel based on their experience;

[0154] At the same time, the sample pixel domain and the corresponding pixel domain feature data are stored as the sample pixel domain and sample feature data in the sample database;

[0155] This embodiment combines the advantages of embodiment 1 and embodiment 2, and when all similarities are less than or equal to the similarity threshold, the diagnosis results are determined by relevant personnel based on experience and stored in the sample database, which helps to enrich the sample database and improve the accuracy of subsequent diagnosis.

[0156] For example, for some special or rare neurological imaging conditions, it is possible to make empirical judgments and supplement sample data, so that the system will have more references when facing similar situations in the future.

[0157] Example 4

[0158] As the fourth embodiment of the present invention, when this application is specifically implemented, compared with the first, second and third embodiments, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned first, second and third embodiments.

[0159] This embodiment combines the solutions of embodiment 1, embodiment 2, and embodiment 3, and can give full play to the advantages of each step to achieve more comprehensive, accurate, and efficient nervous system imaging analysis and diagnosis.

[0160] For example, in complex clinical diagnostic scenarios, various types of nervous system images can be processed more effectively, providing more reliable support for the diagnosis and treatment of diseases.

[0161] See also Figure 2 As shown, the present invention also provides a method for analyzing nervous system images, which is implemented by the nervous system image analysis system described above, and includes the following steps:

[0162] Step 1: Data Acquisition

[0163] Acquire images of the nervous system to be analyzed;

[0164] Step 2: Image processing

[0165] Converting the nervous system image into a grayscale image, then performing image normalization on the grayscale value of each pixel on the grayscale image, and obtaining the normalized grayscale value of each pixel;

[0166] Step 3: Segmentation

[0167] According to the normalized grayscale values ​​of all pixels corresponding to the nervous system image, the nervous system image is segmented and a segmentation mark corresponding to each pixel is obtained, and then multiple pixel domains are obtained from the nervous system image according to the segmentation mark;

[0168] Step 4: Feature extraction

[0169] Perform feature extraction on all pixel domains in the nervous system image and obtain pixel domain feature data;

[0170] Step 5: Sample database preparation

[0171] A large amount of historical neurological imaging data containing diagnostic results is collected in advance, and all pixel domains and their corresponding pixel domain feature data are obtained from the historical neurological imaging data through image processing, segmentation processing, and feature extraction. The obtained pixel domains and their corresponding pixel domain feature data are respectively the sample pixel domains and sample feature data. At the same time, the diagnostic results obtained in advance from the historical neurological imaging data are used to add diagnostic result labels to each pixel domain.

[0172] Step 6: Analysis and Evaluation

[0173] The pixel domains and their pixel domain feature data corresponding to the nervous system image to be analyzed are diagnosed and analyzed with the sample pixel domains and sample feature data in the sample database, and the diagnosis result of the pixel domain corresponding to the nervous system image to be analyzed is determined.

[0174] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0175] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A nervous system image analysis system, characterized in that: include: a segmentation processing unit for segmenting the nervous system image based on the grayscale values ​​of all pixels corresponding to the nervous system image, obtaining a segmentation identifier corresponding to each pixel, and then obtaining a plurality of pixel domains from the nervous system image based on the segmentation identifier, wherein a pixel domain refers to an image block composed of pixels having the same segmentation identifier; A feature extraction unit is used to perform feature extraction processing on all pixel domains in the nervous system image and obtain pixel domain feature data; A sample database, used to store pre-trained sample pixel domains and their corresponding sample feature data, wherein the sample pixel domains are annotated with diagnostic results; an analysis and evaluation unit, configured to perform diagnostic analysis on each pixel domain and its pixel domain feature data corresponding to the nervous system image to be analyzed and the sample pixel domain and sample feature data in the sample database, and determine a diagnostic result of the pixel domain corresponding to the nervous system image to be analyzed; The diagnostic analysis is as follows: Step D1, select a pixel domain and its pixel domain feature data corresponding to a neural system image to be analyzed, and a sample pixel domain and its corresponding sample feature data as an example; Then, the shape factor, area, and perimeter of the pixel domain feature data corresponding to the nervous system image to be analyzed are obtained and marked as X0, S0, and C0 respectively; Step D2: Mark the shape factor, area, and perimeter of the sample feature data corresponding to a sample pixel domain as X. y 、S y and C y ; Step D3, then pass K=1 / (|X0-X y |×β1+|S0-S y |×β2+|C0-C y |×β3) Calculating the similarity K between the pixel domain corresponding to the nervous system image to be analyzed and the sample pixel domain; Where β1, β2, and β3 are the corresponding preset weight coefficients; Step D4, through X0 and X y The absolute value of the difference between y The absolute value of the difference between C0 and S0, y The absolute value of the difference between them is used to screen out the sample pixel domain involved in the similarity calculation: Select a sample pixel domain; If |X0-X y |>XL,|S0-S y |>SL,|C0-C y |>If any of the items in CL is true, the sample pixel domain will not be extracted to participate in the similarity calculation; If |X0-X y |>XL,|S0-S y |>SL,|C0-C y |>CL does not hold, then the sample pixel domain is extracted to participate in the similarity calculation; Among them, XL, SL, and CL are the corresponding preset difference screening thresholds; Then, according to Step D1 to Step D3, the similarity between the pixel domain corresponding to the nervous system image to be analyzed and the corresponding pixel domains of all samples is calculated; Step D5. Compare each obtained similarity with a preset similarity threshold, and based on the comparison result, obtain the corresponding sample pixel domain when the similarity is greater than the similarity threshold, then obtain a sample pixel domain with the largest similarity value, and then obtain the diagnostic result annotation of the sample pixel domain, and use the diagnostic result annotation of the sample pixel domain as the diagnostic result of the corresponding pixel domain of the nervous system image to be analyzed.

2. A nervous system image analysis system according to claim 1, characterized in that: Also includes: a data acquisition unit, for acquiring nervous system images to be analyzed; The image processing unit is used to convert the nervous system image into a grayscale image, and then perform image normalization on the grayscale value of each pixel on the grayscale image to obtain the grayscale value of each pixel after normalization.

3. A nervous system image analysis system according to claim 2, characterized in that: Image normalization is done as follows: Step A1: Get the grayscale value of each pixel in the grayscale image and mark the grayscale value of each pixel as G in the coordinate system. (i,j) , where (i, j) represents the pixel coordinates in the image, i represents the nth horizontal coordinate, j represents the nth vertical coordinate, i = 1, 2, ... n, j = 1, 2, ... m, and n × m is the number of all pixels in the nervous system image; Where i = 1, 2, ... n, n represents the number of all pixels; Step A2: Get the grayscale value with the largest value and the grayscale value with the smallest value from the grayscale values ​​of all pixels, and mark them as G and G respectively. max and Gmin ; Step A3, through H (i,j) =(G (i,j) -G min ) / (G max -G min ), and obtain the gray value H of each pixel after normalization (i,j) .

4. A nervous system image analysis system according to claim 3, characterized in that: The segmentation process is as follows: Step B1: Select a pixel from the nervous system image as a reference pixel and obtain the normalized grayscale value H corresponding to the reference pixel. (i,j) ; At the same time, add segmentation marks for the benchmark pixels; Among them, the segmentation mark is a specific mark assigned to the pixel point to distinguish different pixel domains during the neural system image segmentation process; Step B2, based on H (i,j) , obtain the grayscale values ​​H of multiple pixels adjacent to the reference pixel (i-1,j) 、H (i+1,j) 、H (i,j-1) 、H (i,j+1) 、H (i+1,j+1) 、H (i+1,j-1) 、H (i-1,j-1) 、H (i-1,j+1) ; Step B3, calculate the gray value H corresponding to the reference pixel points respectively (i,j) The grayscale values ​​H of adjacent pixels are (i-1,j) 、H (i+1,j) 、H (i,j-1) 、H (i,j+1) 、H (i+1,j+1) 、H (i+1,j-1) 、H (i-1,j-1) 、H (i-1,j+1) The absolute value of the difference between them is then compared with the preset grayscale difference threshold Hy: Select H from (i,j) and H (i+1,j) ; If |H (i,j) -H (i+1,j) |>Hy, then a segmentation marker different from the reference pixel point is added to the pixel point adjacent to the reference pixel point; If |H (i,j) -H (i+1,j) |≤Hy, then the pixel point adjacent to the reference pixel point will be added with the same segmentation mark as the reference pixel point; Step B4: Using each adjacent pixel as a reference pixel in the same manner as Step B3, and obtaining segmentation identifiers of each pixel adjacent to the reference pixel; And so on, until all pixels are added with segmentation marks; Step B5: Based on the segmentation identifier, all pixels with the same segmentation identifier are grouped into a pixel domain. The pixel domain refers to an image block composed of pixels with the same segmentation identifier.

5. A nervous system image analysis system according to claim 4, characterized in that: In Step B4, the pixel points that have been added with the segmentation mark are not subjected to the secondary addition of the segmentation mark. When the adjacent pixel points are used as the reference pixel points, the reference pixel points selected in the early stage are not subjected to the addition of the segmentation mark.

6. A nervous system image analysis system according to claim 1, characterized in that: The pixel domain feature data includes: the area, perimeter and shape factor of the pixel domain; The feature extraction process is as follows: Select a pixel region; Step C1, obtain the number of all pixels in the pixel domain, and then record the number as the area of ​​the pixel domain; Step C2, obtaining the number of all pixel points on the boundary of the pixel domain, and then recording the number as the perimeter of the pixel domain; The method for determining whether a pixel point in the pixel domain is on the boundary is as follows: If at least one of the multiple adjacent pixels of a pixel is not in the pixel domain, the pixel is determined to be on the boundary. If all adjacent pixels are in the pixel domain, it is determined that the pixel is not on the boundary; Step C3, calculate the shape factor of the pixel domain based on the area and perimeter of the pixel domain; Calculation formula: X = 4π × S / C2 X represents the shape factor of the pixel region, S represents the area of ​​the pixel region, and C represents the perimeter of the pixel region.

7. A nervous system image analysis system according to claim 6, characterized in that: in, The pre-training method of the sample pixel domain and the sample feature data is to collect a large amount of historical neurological imaging data containing diagnostic results in advance, and obtain all pixel domains and their corresponding pixel domain feature data from the historical neurological imaging data through an image processing unit, a segmentation processing unit, and a feature extraction unit. The obtained pixel domains and their corresponding pixel domain feature data are respectively the sample pixel domain and the sample feature data; At the same time, the diagnostic result obtained in advance through the historical data of nervous system imaging is added to each pixel domain. The diagnostic result annotation is added manually. Among them, the historical data of nervous system imaging include normal images and images of different types of nervous system diseases.

8. The nervous system image analysis system according to claim 1, characterized in that: In Step D4, when all similarities are less than or equal to the similarity threshold, it means that the sample pixel domain does not contain a similar sample pixel domain in the sample database, and then the diagnosis result is determined by relevant personnel based on their experience; At the same time, the sample pixel domain and its corresponding pixel domain feature data are stored in the sample database as the sample pixel domain and sample feature data.

9. A method for analyzing nervous system images, characterized in that: The method is implemented by a nervous system image analysis system according to any one of claims 1 to 8.

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