Method for improving delineation precision of tumor radiotherapy target region

Abstract

The invention relates to the technical field of tumor image processing, in particular to a method for improving tumor radiotherapy target area sketching precision. The method comprises the following steps: acquiring an edge connecting line for connecting a central pixel point and different edge pixel points in a possible tumor region; according to the gray level distribution of the pixel points on each edge connecting line, the pixel gray level uniformity of each edge connecting line is obtained, and a connecting line cluster is obtained; analyzing the position distribution characteristics of edge connecting lines in different connecting line clusters and the number of the connecting line clusters, and obtaining the overall gray uniformity of each possible tumor region; obtaining the tumor credibility of each tumor possible region by combining the gray distribution of the boundary pixel points in each tumor possible region and the boundary morphological characteristics of each tumor possible region; an optimal tumor area is obtained, and a tumor target area is sketched. According to the invention, the accurate gray scale range of the tumor area is obtained, so that the target area sketching precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of tumor image processing, and in particular to a method for improving the accuracy of tumor radiotherapy target area delineation. Background Art

[0002] The tumor radiotherapy target area refers to the area that needs to be irradiated during tumor radiotherapy. The delineation of the tumor radiotherapy target area is a key step in the process of radiotherapy planning. Accurate target area delineation can ensure that tumor tissues can receive sufficient doses of irradiation, killing tumor cells to the greatest extent while minimizing irradiation to surrounding normal tissues and organs and protecting normal tissues.

[0003] In the prior art, the tumor target area is divided based on the threshold and is delineated using a semi-automatic target area delineation technique. However, due to the low contrast between some tumor tissues and normal tissues, such as in the magnetic resonance imaging of some brain tumors, the signal difference between the tumor and the surrounding edema tissue is not obvious, making it difficult to clearly identify the tumor boundary, and the accuracy of target area delineation is poor. Summary of the invention

[0004] In order to solve the technical problem that the contrast between some tumor tissues and normal tissues is low and the accuracy of target area delineation is poor, the purpose of the present invention is to provide a method for improving the accuracy of tumor radiotherapy target area delineation. The technical scheme adopted is as follows: The present invention proposes a method for improving the accuracy of tumor radiotherapy target area delineation, the method comprising: Obtain grayscale images of the patient's brain magnetic resonance imaging; According to the grayscale distribution characteristics of the pixels in the grayscale image, multiple possible tumor areas are obtained; for any possible tumor area, edge lines connecting the central pixel and different edge pixels are obtained; according to the grayscale distribution of the pixels on each edge line, the pixel grayscale uniformity of each edge line is obtained; according to the pixel grayscale uniformity of each edge line, all edge lines are clustered to obtain line clustering clusters; according to the position distribution characteristics of the edge lines in different line clustering clusters, the pixel grayscale uniformity of all edge lines and the number of line clustering clusters, the overall grayscale uniformity of each possible tumor area is obtained; According to the grayscale distribution of the boundary pixels in each possible tumor area, the boundary clarity of each possible tumor area is obtained; according to the boundary morphological characteristics, overall grayscale uniformity and boundary clarity of each possible tumor area, the tumor credibility of each possible tumor area is obtained; according to the grayscale values ​​of the pixels and the tumor credibility in different possible tumor areas, the optimal tumor area is obtained; Tumor target volumes were delineated based on the optimal tumor region.

[0005] Furthermore, the method for obtaining the possible tumor area includes: The Otsu algorithm is used to obtain the optimal segmentation threshold in the grayscale image. If the grayscale value of a pixel is greater than the optimal segmentation threshold, the corresponding pixel is regarded as a possible tumor pixel. Connected domain detection is performed on all possible tumor pixels to obtain multiple pixel connected domains as possible tumor areas.

[0006] Furthermore, the method for obtaining pixel grayscale uniformity includes: Obtain the grayscale value sequence of the pixel points on each edge line in order of position. If there is a grayscale value in the grayscale value sequence that is greater than or less than the adjacent grayscale values, the corresponding grayscale value is taken as the grayscale extreme value. The difference mean between all adjacent gray values ​​in the gray value sequence is obtained, the product of the difference mean and the number of gray extreme values ​​is calculated, and a negative correlation mapping is performed as the pixel gray uniformity of each edge line.

[0007] Furthermore, the method for obtaining the overall grayscale uniformity includes: According to the position distribution characteristics of the edge links in each link cluster, the link aggregation degree of each link cluster is obtained; According to the connection aggregation of different connection clusters in each possible tumor area, the pixel grayscale uniformity of all edge connections and the number of connection clusters, the overall grayscale uniformity of each possible tumor area was obtained. The connection aggregation and pixel grayscale uniformity were positively correlated with the overall grayscale uniformity, and the number of connection clusters was negatively correlated with the overall grayscale uniformity.

[0008] Furthermore, the method for obtaining the connection concentration degree includes: For each link cluster, select any edge link as the starting point and number all edge links in a clockwise direction. If there are other edge links in the neighborhood of the edge link in the clockwise direction, the corresponding edge link will be used as the adjacent edge link, and all adjacent edge links of each edge link will be obtained to form an adjacent edge link group; the cumulative sum of the differences in the numbers of adjacent edge links in all adjacent edge link groups is obtained as the link clustering degree.

[0009] Furthermore, the method for obtaining the boundary clarity includes: Obtain the grayscale value variance of all boundary pixels in each possible tumor area as the grayscale fluctuation feature; The grayscale gradient mean of all boundary pixels in each possible tumor area is obtained as the grayscale change rate; The ratio between the grayscale change rate and the grayscale fluctuation characteristics of each possible tumor region is obtained as the boundary clarity of each possible tumor region.

[0010] Furthermore, the method for obtaining the tumor credibility includes: Obtain the difference between the 8-chain code value of each boundary pixel and the adjacent boundary pixel, and normalize it. If the normalized result is greater than the preset difference threshold, the corresponding boundary pixel is regarded as an irregular point; all adjacent irregular points are connected to form corresponding lines as irregular lines; The product of the mean value of the number of irregular points on all irregular lines and the number of irregular points is obtained as the first product; the ratio of the first product and the mean value of the number of boundary pixel points between all adjacent irregular lines is obtained as the irregular density; The tumor credibility of each possible tumor area is obtained according to the overall grayscale uniformity, boundary clarity and irregular density of each possible tumor area. The irregular density is positively correlated with the tumor credibility, while the overall grayscale uniformity and boundary clarity are negatively correlated with the tumor credibility.

[0011] Furthermore, the method for obtaining the optimal tumor region includes: The target tumor area is screened out according to the tumor credibility of each possible tumor area; According to the tumor credibility of the target tumor area, the initial minimum grayscale value and the initial maximum grayscale value of the pixel points in all the target tumor areas are adjusted to obtain the minimum grayscale transformation value and the maximum grayscale transformation value; In the grayscale image, the corresponding area within the range formed by the grayscale transformation minimum value and the grayscale transformation maximum value is obtained as the optimal tumor area.

[0012] Furthermore, the method for acquiring the target tumor area includes: If the tumor credibility of the possible tumor region is greater than the preset credibility threshold, the corresponding possible tumor region is used as the target tumor region.

[0013] Furthermore, the method for obtaining the grayscale transformation minimum value and the grayscale transformation maximum value includes: The mean tumor credibility of all target tumor regions is obtained as the average credibility level; Obtain the difference between the positive integer 1 and the average confidence level as the first difference; calculate the product of the grayscale initial minimum value and the first difference, and round it down to obtain the grayscale transformation minimum value; The difference between the grayscale standard maximum value and the grayscale initial maximum value is obtained as the second difference; the product of the second difference and the average credible level is calculated as the first product; the sum of the grayscale initial maximum value and the first product is calculated and rounded down as the grayscale transformation maximum value.

[0014] The present invention has the following beneficial effects: The present invention obtains multiple possible tumor areas according to the grayscale distribution characteristics of pixel points in the grayscale image, and preliminarily locates the area in the image that may be related to the tumor; in order to quantify the shape, size and distribution of the tumor area, for any possible tumor area, the edge lines connecting the central pixel point and different edge pixel points are obtained; according to the grayscale distribution of the pixel points on each edge line, the pixel grayscale uniformity of each edge line is obtained to reflect the grayscale change of the edge area; according to the pixel grayscale uniformity of each edge line, all edge lines are clustered to obtain line clustering clusters, and edge lines with similar grayscale characteristics can be grouped for discussion through clustering; according to the position distribution characteristics of edge lines in different line clusters and the pixel grayscale uniformity of all edge lines, the edge lines are clustered to obtain the edge clustering clusters. And the number of connected clusters, the overall grayscale uniformity of each possible tumor area is obtained, and the grayscale distribution of the entire possible tumor area is quantified more comprehensively; according to the grayscale distribution of the boundary pixels in each possible tumor area, the boundary clarity of each possible tumor area is obtained, and the boundary characteristics of the possible tumor area are analyzed, which is helpful to evaluate the tumor boundary characteristics of the possible tumor area; according to the boundary morphological characteristics, overall grayscale uniformity and boundary clarity of each possible tumor area, the tumor credibility of each possible tumor area is obtained, and the possibility of whether a certain area is a tumor is evaluated; according to the grayscale values ​​and tumor credibility of the pixels in different possible tumor areas, the optimal tumor area is obtained, so that the tumor area is more prominent in the image; the tumor target area is delineated. The present invention improves the accuracy of target area delineation by obtaining an accurate grayscale range of the tumor area. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0016] Figure 1 A flow chart of a method for improving the accuracy of tumor radiotherapy target area delineation provided by one embodiment of the present invention.

[0017] Figure 2 A flow chart of a method for obtaining tumor credibility provided by an embodiment of the present invention.

[0018] Figure 3 A flow chart of a method for obtaining grayscale transformation values ​​provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a method for improving the accuracy of tumor radiotherapy target area delineation proposed by the present invention, its specific implementation method, structure, features and effects, in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0020] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0021] A specific scheme of a method for improving the accuracy of tumor radiotherapy target area delineation provided by the present invention is described in detail below with reference to the accompanying drawings.

[0022] See also Figure 1 , which shows a flow chart of a method for improving the accuracy of tumor radiotherapy target area delineation provided by an embodiment of the present invention, and the specific method includes: Step S1: Obtaining a grayscale image of the patient's brain magnetic resonance imaging.

[0023] In the embodiment of the present invention, in order to accurately delineate the tumor target area, it is necessary to identify the accurate tumor area; first, perform a nuclear magnetic resonance examination on the patient to obtain the patient's brain nuclear magnetic resonance image to ensure the quality of the acquired image; It should be noted that, in order to facilitate subsequent image processing, in one embodiment of the present invention, the brain nuclear magnetic image is grayed to obtain a gray image of the brain nuclear magnetic, and then the processed gray image is analyzed. It should be noted that graying is a technical means well known to those skilled in the art, and can be specifically set according to the specific implementation scenario. In one embodiment of the present invention, a mean graying algorithm is used to obtain a gray image, simplify image information, increase computing speed, and help improve the accuracy of image recognition.

[0024] Step S2: According to the grayscale distribution characteristics of the pixels in the grayscale image, multiple possible tumor areas are obtained; for any possible tumor area, edge lines connecting the central pixel and different edge pixels are obtained; according to the grayscale distribution of the pixels on each edge line, the pixel grayscale uniformity of each edge line is obtained; according to the pixel grayscale uniformity of each edge line, all edge lines are clustered to obtain line clustering clusters; according to the position distribution characteristics of the edge lines in different line clustering clusters, the pixel grayscale uniformity of all edge lines and the number of line clustering clusters, the overall grayscale uniformity of each possible tumor area is obtained.

[0025] Since tumor cells have active metabolism and high water content, they usually appear lighter in grayscale images, while the surrounding non-tumor areas have slightly darker grayscales. Therefore, multiple possible tumor areas are obtained based on the grayscale distribution characteristics of pixels in the grayscale image.

[0026] Preferably, in one embodiment of the present invention, the method for acquiring a possible tumor region includes: The Otsu algorithm is used to obtain the optimal segmentation threshold in the grayscale image. If the grayscale value of a pixel is greater than the optimal segmentation threshold, the corresponding pixel is regarded as a possible tumor pixel. Connected domain detection is performed on all possible tumor pixels to obtain multiple pixel connected domains as possible tumor areas.

[0027] It should be noted that the specific Otsu algorithm and connected domain algorithm are technical means well known to those skilled in the art and will not be described in detail here.

[0028] In order to quantify the shape, size and distribution of the tumor area, for any possible tumor area, the edge lines connecting the central pixel and different edge pixels are obtained.

[0029] Due to the differences in cell composition, blood vessel distribution, and the presence or absence of necrotic cystic changes inside the tumor, the grayscale values ​​in different areas are inconsistent, while the grayscale distribution of brain tissue in non-tumor areas is relatively uniform. The grayscale distribution of pixels on each edge line reflects the grayscale change characteristics of the pixels. Based on the grayscale distribution of pixels on each edge line, the pixel grayscale uniformity of each edge line is obtained.

[0030] Preferably, in one embodiment of the present invention, the method for obtaining pixel grayscale uniformity includes: Obtain the grayscale value sequence of the pixel points on each edge line in order of position. If there is a grayscale value in the grayscale value sequence that is greater than or less than the adjacent grayscale values, the corresponding grayscale value is taken as the grayscale extreme value. The difference mean between all adjacent gray values ​​in the gray value sequence is obtained, the product of the difference mean and the number of gray extreme values ​​is calculated, and a negative correlation mapping is performed as the pixel gray uniformity of each edge line.

[0031] In one embodiment of the present invention, the formula for pixel grayscale uniformity is expressed as: ; in, Indicates The uniformity of pixel grayscale along the edge line; Indicates The number of grayscale extreme values ​​of the edge lines; Indicates The difference between a gray value and the next adjacent gray value; Indicates The number of gray values ​​in the gray value sequence corresponding to the edge lines; Represents an exponential function with a natural constant as its base.

[0032] In the formula for pixel grayscale uniformity, the exponential function with a natural constant as the base is used to convert Perform negative correlation mapping; Indicates The edge line corresponds to the mean difference between all adjacent gray values ​​in the gray value sequence. The larger the mean difference, the larger the gray value between adjacent pixels, and the more uneven the gray distribution of pixels. The greater the number of grayscale extreme values ​​of the edge lines, the greater the grayscale fluctuation and the smaller the pixel grayscale uniformity.

[0033] By clustering, edge lines with similar grayscale distribution characteristics can be grouped into one category, and a large number of edge lines can be divided into a few clusters to simplify the analysis process; according to the uniformity of the pixel grayscale of each edge line, all edge lines are clustered to obtain line clustering clusters.

[0034] It should be noted that, in one embodiment of the present invention, the method for obtaining line clustering clusters includes: performing K-means clustering on all edge lines according to the pixel grayscale uniformity of each edge line to obtain multiple line clustering clusters; in other embodiments of the present invention, clustering can also be performed by existing clustering methods such as mean shift clustering. The specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0035] The position distribution characteristics of edge lines within different line clusters reflect the shape and structural complexity of the tumor area. The pixel grayscale uniformity reflects the grayscale change of the possible tumor area. The greater the pixel grayscale uniformity, the more uniform the pixel grayscale distribution, and the more likely it is a normal tissue area. The more line clusters there are, the greater the difference in pixel grayscale uniformity of different edge lines, and the more uneven the grayscale of the possible tumor area. According to the position distribution characteristics of edge lines within different line clusters, the pixel grayscale uniformity of all edge lines and the number of line clusters, the overall grayscale uniformity of each possible tumor area is obtained.

[0036] Preferably, in one embodiment of the present invention, the method for obtaining the overall grayscale uniformity includes: According to the position distribution characteristics of the edge links in each link cluster, the link aggregation degree of each link cluster is obtained; Preferably, in one embodiment of the present invention, the method for obtaining the connection concentration includes: For each link cluster, select any edge link as the starting point and number all edge links in a clockwise direction. If there are other edge links in the neighborhood of the edge link in the clockwise direction, the corresponding edge link will be used as the adjacent edge link, and all adjacent edge links of each edge link will be obtained to form an adjacent edge link group; the cumulative sum of the differences in the numbers of adjacent edge links in all adjacent edge link groups is obtained as the link clustering degree.

[0037] It should be noted that, in one embodiment of the present invention, the neighborhood range is based on the edge pixel points on the boundary of the edge line, and the Euclidean distance between the edge pixel points corresponding to other edge lines is less than a preset distance, wherein the preset distance is 0.3; in other embodiments of the present invention, the neighborhood range can be set according to specific circumstances, and is not limited or elaborated here.

[0038] According to the connection aggregation of different connection clusters in each possible tumor area, the pixel grayscale uniformity of all edge connections and the number of connection clusters, the overall grayscale uniformity of each possible tumor area was obtained. The connection aggregation and pixel grayscale uniformity were positively correlated with the overall grayscale uniformity, and the number of connection clusters was negatively correlated with the overall grayscale uniformity.

[0039] In one embodiment of the present invention, the formula for overall grayscale uniformity is expressed as: ; in, Indicates the overall grayscale uniformity of each possible tumor area; Indicates The uniformity of pixel grayscale along the edge line; Indicates the number of edge lines in the possible tumor area; Indicates The degree of connection clustering of the connection clusters; Indicates the number of link clusters.

[0040] In the formula for overall grayscale uniformity, It means calculating the mean value of pixel grayscale uniformity of all edge lines in the possible tumor area; It represents the mean value of the connection aggregation of all connected clusters in the possible tumor area. The greater the pixel grayscale uniformity, the greater the connection aggregation, the more uniform the grayscale distribution on the edge line, which means the greater the overall grayscale uniformity, which is positively correlated. The more connected clusters there are, the greater the difference in grayscale uniformity of different edge lines, the worse the overall grayscale uniformity, which is negatively correlated.

[0041] Step S3: According to the grayscale distribution of the boundary pixels in each possible tumor area, the boundary clarity of each possible tumor area is obtained; according to the boundary morphological characteristics, overall grayscale uniformity and boundary clarity of each possible tumor area, the tumor credibility of each possible tumor area is obtained; according to the grayscale values ​​and tumor credibility of the pixels in different possible tumor areas, the optimal tumor area is obtained.

[0042] The boundaries of benign tumors may be relatively clear, with a certain demarcation from surrounding tissues, but they may also appear as a gradual transition of grayscale at the boundaries on grayscale images. The boundaries of malignant tumors are usually blurred and grow in an invasive manner. The grayscale boundary with the surrounding normal tissues is not obvious, and it is difficult to accurately define the scope of the tumor. The edge of the tumor may experience sudden changes in grayscale values ​​or irregular grayscale transition zones. The boundaries of normal brain tissues are relatively clear and regular in shape. There is generally no blurred or abnormal boundary shape, and the grayscale value changes are relatively stable. The boundary clarity of each possible tumor area is obtained based on the grayscale distribution of boundary pixels in each possible tumor area.

[0043] Preferably, in one embodiment of the present invention, the method for acquiring boundary definition includes: Obtain the grayscale value variance of all boundary pixels in each possible tumor area as the grayscale fluctuation feature; The grayscale gradient mean of all boundary pixels in each possible tumor area is obtained as the grayscale change rate; The ratio between the grayscale change rate and the grayscale fluctuation characteristics of each possible tumor region is obtained as the boundary clarity of each possible tumor region.

[0044] The larger the grayscale value variance, the more uneven the grayscale distribution, the larger the grayscale fluctuation characteristics, and the more regular the boundary shape. Conversely, the more uniform the grayscale distribution, the smaller the grayscale fluctuation characteristics, and the more irregular the boundary. The larger the grayscale gradient mean, the more drastic the grayscale change at the boundary, the greater the contrast at the boundary, and the clearer the boundary. Therefore, the smaller the grayscale fluctuation characteristics, the greater the grayscale change rate, and the greater the boundary clarity.

[0045] It should be noted that when calculating the ratio between the grayscale change rate and the grayscale fluctuation characteristics, in order to avoid the denominator of the formula being 0 and the formula being meaningless, a threshold value, such as 0.01, can be added artificially. The specific means are technical means well known to technical personnel in this field and will not be elaborated here.

[0046] The boundary morphology of the brain tumor area is irregular, and the boundary is fuzzy relative to the normal brain tissue area; the overall grayscale uniformity reflects the uniformity of the pixel grayscale values ​​in the entire possible tumor area. The greater the overall grayscale uniformity, the smaller the possibility of a tumor; therefore, the tumor credibility of each possible tumor area is obtained based on the boundary morphological characteristics, overall grayscale uniformity and boundary clarity of each possible tumor area.

[0047] Preferably, in one embodiment of the present invention, the method for obtaining the tumor credibility is as follows: Figure 2 , which shows a flow chart of a method for obtaining tumor credibility, including: Step S201: Obtain the difference in the 8-chain code value between each boundary pixel point and the adjacent boundary pixel point, and normalize it. If the normalized result is greater than the preset difference threshold, the corresponding boundary pixel point is regarded as an irregular point; all adjacent irregular points are connected to form corresponding lines as irregular lines.

[0048] The 8-chain code of the boundary pixel point can indicate the boundary direction of the possible tumor area. The smaller the difference of the 8-chain code values ​​between adjacent boundary pixels, the smaller the direction change between adjacent boundary pixels, and the more regular the boundary is. Conversely, the greater the difference, the greater the direction change between adjacent boundary pixels, and the more irregular the boundary is, which reflects that there may be a tumor in the area and is marked as an irregular point.

[0049] It should be noted that, in one embodiment of the present invention, the size of the preset difference threshold is 0.1; in other embodiments of the present invention, the size of the preset difference threshold can be set according to specific circumstances, which is not limited or elaborated here.

[0050] Step S202: obtaining the product of the mean value of the number of irregular points on all irregular lines and the number of irregular points as the first product; obtaining the ratio of the first product to the mean value of the number of boundary pixels between all adjacent irregular lines as the irregular density.

[0051] The more irregular points there are, the more area the irregular lines occupy on the boundary, the fewer boundary pixels between adjacent irregular lines, the more discontinuous the boundary is, and the greater the credibility of the tumor.

[0052] Step S203: Obtain the tumor credibility of each possible tumor region according to the overall grayscale uniformity, boundary clarity and irregularity density of each possible tumor region. The irregularity density is positively correlated with the tumor credibility, and the overall grayscale uniformity and boundary clarity are negatively correlated with the tumor credibility.

[0053] In one embodiment of the present invention, the formula for tumor credibility is expressed as: ; in, Indicates The tumor confidence level of each possible tumor area; Indicates The overall grayscale uniformity of each possible tumor area; Indicates The clarity of the boundaries of the possible tumor area; Indicates The mean number of irregular points on all irregular lines in a possible tumor area; Indicates The number of irregular points in a possible tumor area; Represents the mean number of boundary pixels between all adjacent irregular lines; represents the normalization function; Represents an exponential function with a natural constant as its base.

[0054] In the formula for tumor credibility, Indicates that the The product of the mean number of irregular points on all irregular lines in a possible tumor area and the number of irregular points is taken as the first product; the ratio of the first product to the mean number of boundary pixel points between all adjacent irregular lines is obtained, that is, the irregular density. The greater the irregular density, the more irregular points there are, the more area the irregular lines occupy on the boundary, the fewer boundary pixel points between adjacent irregular lines, the more discontinuous the boundary, and the greater the tumor credibility; the exponential function with natural constant as the base is used to convert When performing negative correlation mapping, the greater the boundary clarity and the greater the overall grayscale uniformity, the smaller the possibility of a tumor and the lower the credibility of the tumor.

[0055] The grayscale value reflects the brightness information of the pixel. The tumor area usually has a different grayscale value from the surrounding tissue. The tumor area appears as a larger grayscale value in the image. In order to make the tumor area easier to identify, the grayscale value of the tumor area is adjusted according to the tumor credibility. The tumor credibility provides the probability information that the tumor possible area belongs to the tumor. The greater the tumor credibility, the more likely it is a tumor area, and the more image enhancement is required for the area. The best tumor area is obtained based on the grayscale value of the pixel points in different tumor possible areas and the tumor credibility.

[0056] Preferably, in one embodiment of the present invention, the method for obtaining the optimal tumor region is as follows. Figure 3 , which shows a flow chart of a method for obtaining an optimal tumor region, including: Step S301: Screening out target tumor regions according to the tumor credibility of each possible tumor region.

[0057] Preferably, in one embodiment of the present invention, the method for acquiring the target tumor region includes: If the tumor credibility of the possible tumor region is greater than the preset credibility threshold, the corresponding possible tumor region is used as the target tumor region.

[0058] It should be noted that, in one embodiment of the present invention, the size of the preset trustworthy threshold is 0.8; in other embodiments of the present invention, the size of the preset trustworthy threshold can be set according to specific circumstances, which is not limited or elaborated here.

[0059] Step S302: According to the tumor credibility of the target tumor area, the grayscale initial minimum value and the grayscale initial maximum value of the pixel points in all the target tumor areas are adjusted to obtain the grayscale transformation minimum value and the grayscale transformation maximum value.

[0060] Preferably, in one embodiment of the present invention, the method for obtaining the grayscale transformation minimum value and the grayscale transformation maximum value includes: The mean tumor credibility of all target tumor regions is obtained as the average credibility level; Obtain the difference between the positive integer 1 and the average confidence level as the first difference; calculate the product of the grayscale initial minimum value and the first difference, and round it down to obtain the grayscale transformation minimum value; The difference between the grayscale standard maximum value and the grayscale initial maximum value is obtained as the second difference; the product of the second difference and the average credible level is calculated as the first product; the sum of the grayscale initial maximum value and the first product is calculated and rounded down as the grayscale transformation maximum value.

[0061] In one embodiment of the present invention, the formulas for the grayscale transformation minimum value and the grayscale transformation maximum value are expressed as: ; ; in, Represents the minimum value of grayscale transformation; Indicates the maximum value of grayscale transformation; Indicates the initial minimum value of grayscale; Grayscale initial maximum value; It represents the mean tumor credibility of all target tumor areas, that is, the average credibility level; Indicates the maximum value of grayscale standard; Indicates the floor symbol.

[0062] In the formulas of the maximum grayscale transformation and the minimum grayscale transformation, the greater the credibility, the larger the maximum grayscale transformation is adjusted, the smaller the minimum grayscale transformation is adjusted, and the larger the grayscale range is, so as to avoid missing tumor pixels with smaller or larger grayscales.

[0063] It should be noted that the grayscale standard maximum value is the maximum value within the grayscale value standard range of the grayscale image, that is, 255.

[0064] Step S303: In the grayscale image, a corresponding area within the range formed by the grayscale transformation minimum value and the grayscale transformation maximum value is obtained as the optimal tumor area.

[0065] Step S4: outlining the tumor target area based on the optimal tumor area.

[0066] The optimal tumor area reflects a more complete tumor region and contributes to more accurate target delineation.

[0067] Using the commonly used semi-automatic target delineation technology to delineate the tumor target area of ​​the patient's optimal tumor area helps to improve the delineation accuracy of the tumor target area. The specific means are well known to those skilled in the art and will not be described in detail here.

[0068] In summary, for any possible tumor area, the present invention obtains edge lines connecting the central pixel and different edge pixels; according to the grayscale distribution of the pixels on each edge line, the pixel grayscale uniformity of each edge line is obtained, and the line clustering cluster is obtained; according to the position distribution characteristics of the edge lines in different line clustering clusters, the pixel grayscale uniformity of all edge lines and the number of line clustering clusters, the overall grayscale uniformity of each possible tumor area is obtained; according to the grayscale distribution of the boundary pixels in each possible tumor area, the boundary clarity of each possible tumor area is obtained; combined with the analysis of the boundary morphological characteristics of each possible tumor area, the optimal tumor area is obtained, and the tumor target area is delineated. The present invention improves the accuracy of target area delineation by obtaining an accurate grayscale range of the tumor area.

[0069] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0070] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for improving the accuracy of tumor radiotherapy target area delineation, characterized in that: The method comprises: Obtain grayscale images of the patient's brain magnetic resonance imaging; According to the grayscale distribution characteristics of the pixels in the grayscale image, multiple possible tumor areas are obtained; for any possible tumor area, edge lines connecting the central pixel and different edge pixels are obtained; according to the grayscale distribution of the pixels on each edge line, the pixel grayscale uniformity of each edge line is obtained; according to the pixel grayscale uniformity of each edge line, all edge lines are clustered to obtain line clustering clusters; according to the position distribution characteristics of the edge lines in different line clustering clusters, the pixel grayscale uniformity of all edge lines and the number of line clustering clusters, the overall grayscale uniformity of each possible tumor area is obtained; According to the grayscale distribution of the boundary pixels in each possible tumor area, the boundary clarity of each possible tumor area is obtained; according to the boundary morphological characteristics, overall grayscale uniformity and boundary clarity of each possible tumor area, the tumor credibility of each possible tumor area is obtained; according to the grayscale values ​​of the pixels and the tumor credibility in different possible tumor areas, the optimal tumor area is obtained; Tumor target volumes were delineated based on the optimal tumor region.

2. A method for improving the accuracy of tumor radiotherapy target area delineation according to claim 1, characterized in that: The method for obtaining the possible tumor area includes: The Otsu algorithm is used to obtain the optimal segmentation threshold in the grayscale image. If the grayscale value of a pixel is greater than the optimal segmentation threshold, the corresponding pixel is regarded as a possible tumor pixel. Connected domain detection is performed on all possible tumor pixels to obtain multiple pixel connected domains as possible tumor areas.

3. A method for improving the accuracy of tumor radiotherapy target area delineation according to claim 1, characterized in that: The method for obtaining pixel grayscale uniformity includes: Obtain the grayscale value sequence of the pixel points on each edge line in order of position. If there is a grayscale value in the grayscale value sequence that is greater than or less than the adjacent grayscale values, the corresponding grayscale value is taken as the grayscale extreme value. The difference mean between all adjacent gray values ​​in the gray value sequence is obtained, the product of the difference mean and the number of gray extreme values ​​is calculated, and a negative correlation mapping is performed as the pixel gray uniformity of each edge line.

4. The method for improving the accuracy of tumor radiotherapy target area delineation according to claim 1, characterized in that: The method for obtaining the overall grayscale uniformity includes: According to the position distribution characteristics of the edge links in each link cluster, the link aggregation degree of each link cluster is obtained; According to the connection aggregation of different connection clusters in each possible tumor area, the pixel grayscale uniformity of all edge connections and the number of connection clusters, the overall grayscale uniformity of each possible tumor area was obtained. The connection aggregation and pixel grayscale uniformity were positively correlated with the overall grayscale uniformity, and the number of connection clusters was negatively correlated with the overall grayscale uniformity.

5. A method for improving the accuracy of tumor radiotherapy target area delineation according to claim 4, characterized in that: The method for obtaining the connection concentration degree includes: For each link cluster, select any edge link as the starting point and number all edge links in a clockwise direction. If there are other edge links in the neighborhood of the edge link in the clockwise direction, the corresponding edge link will be used as the adjacent edge link, and all adjacent edge links of each edge link will be obtained to form an adjacent edge link group; the cumulative sum of the differences in the numbers of adjacent edge links in all adjacent edge link groups is obtained as the link clustering degree.

6. The method for improving the accuracy of tumor radiotherapy target area delineation according to claim 1, characterized in that: The method for obtaining the boundary clarity includes: Obtain the grayscale value variance of all boundary pixels in each possible tumor area as the grayscale fluctuation feature; The grayscale gradient mean of all boundary pixels in each possible tumor area is obtained as the grayscale change rate; The ratio between the grayscale change rate and the grayscale fluctuation characteristics of each possible tumor region is obtained as the boundary clarity of each possible tumor region.

7. The method for improving the accuracy of tumor radiotherapy target area delineation according to claim 1, characterized in that: The method for obtaining the tumor credibility includes: Obtain the difference between the 8-chain code value of each boundary pixel and the adjacent boundary pixel, and normalize it. If the normalized result is greater than the preset difference threshold, the corresponding boundary pixel is regarded as an irregular point; all adjacent irregular points are connected to form corresponding lines as irregular lines; The product of the mean value of the number of irregular points on all irregular lines and the number of irregular points is obtained as the first product; the ratio of the first product and the mean value of the number of boundary pixel points between all adjacent irregular lines is obtained as the irregular density; The tumor credibility of each possible tumor area is obtained according to the overall grayscale uniformity, boundary clarity and irregular density of each possible tumor area. The irregular density is positively correlated with the tumor credibility, while the overall grayscale uniformity and boundary clarity are negatively correlated with the tumor credibility.

8. The method for improving the accuracy of tumor radiotherapy target area delineation according to claim 1, characterized in that: The method for obtaining the optimal tumor region comprises: According to the tumor credibility of each possible tumor area, the target tumor area is screened out; According to the tumor credibility of the target tumor area, the initial minimum grayscale value and the initial maximum grayscale value of the pixel points in all the target tumor areas are adjusted to obtain the minimum grayscale transformation value and the maximum grayscale transformation value; In the grayscale image, the corresponding area within the range formed by the grayscale transformation minimum value and the grayscale transformation maximum value is obtained as the optimal tumor area.

9. A method for improving the accuracy of tumor radiotherapy target area delineation according to claim 8, characterized in that: The method for acquiring the target tumor area comprises: If the tumor credibility of the possible tumor region is greater than the preset credibility threshold, the corresponding possible tumor region is used as the target tumor region.

10. The method for improving the accuracy of tumor radiotherapy target area delineation according to claim 8, characterized in that: The method for obtaining the grayscale transformation minimum value and the grayscale transformation maximum value comprises: The mean tumor credibility of all target tumor regions is obtained as the average credibility level; Obtain the difference between the positive integer 1 and the average confidence level as the first difference; calculate the product of the grayscale initial minimum value and the first difference, and round it down to obtain the grayscale transformation minimum value; The difference between the grayscale standard maximum value and the grayscale initial maximum value is obtained as the second difference; the product of the second difference and the average credible level is calculated as the first product; the sum of the grayscale initial maximum value and the first product is calculated and rounded down as the grayscale transformation maximum value.

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