Medical Image Enhancement Method and System for Jaw Cysts Based on Image Processing

Abstract

The present invention relates to the field of enhancement of jaw CT images, and particularly relates to a medical image enhancement method and system for jaw cysts based on image processing. The present invention obtains the gray histogram of an image; clusters based on this to obtain gray clusters; and obtains the gray levels of tissue clusters and non-tissue clusters according to the gray distribution and the number of pixel points in the clusters; obtains an edge coefficient according to the gray difference around the pixel point and the difference between the gray levels of other surrounding pixel points and the non-tissue cluster gray level; connects the pixel points according to the edge coefficient difference between the pixel point and the surrounding pixel points to obtain a regional dividing line between the tissue region and the non-tissue region; and enhances the jaw CT image according to the regional dividing line. The present invention can obtain clear edges in the jaw CT image to facilitate relevant personnel to determine the specific location of the jaw cyst.

Description

Technical Field

[0001] The present invention relates to the field of jaw CT image enhancement, and particularly to a medical image enhancement method and system for jaw cysts based on image processing. Background Art

[0002] In the process of clinical diagnosis and treatment of jaw cysts, medical imaging technology plays a crucial role. First of all, medical images, especially advanced imaging technologies such as cone beam CT, can provide accurate visual information of jaw cysts, helping doctors evaluate the size, location and morphology of the cysts, which is crucial for formulating treatment plans and surgical strategies. Secondly, medical image enhancement processing can improve the image quality, enhance the contrast between the cysts and surrounding tissues, make the boundaries of the cysts clearer, thus improving the diagnostic accuracy, which is crucial for the early detection and precise treatment of cysts, especially when the cyst boundaries are unclear or the contrast with surrounding tissues is low. Through deep learning and image processing technologies, the segmentation and analysis process of jaw cysts can be automated and standardized, reducing the workload of doctors, improving the diagnostic efficiency, and at the same time reducing the diagnostic inconsistency caused by differences in doctor experience. In summary, medical imaging technology not only provides a solid foundation for the clinical diagnosis and treatment of jaw cysts, but also further improves the diagnostic accuracy and treatment effectiveness through the application of image enhancement processing and auxiliary diagnosis technologies, which is of great significance for improving the treatment effect and prognosis of patients.

[0003] Due to a certain degree of similarity in density and structure between jaw cysts and surrounding tissues, it is difficult to distinguish the boundaries. Therefore, the boundaries of jaw cysts are often unclear in images. When directly using edge detection to detect the cyst boundaries, it is necessary to continuously adjust the parameters of the high and low thresholds in the detection algorithm to ensure clear edges, so it lacks adaptability, resulting in an unsatisfactory effect on image enhancement. Summary of the Invention

[0004] To solve the problem that the boundaries of jaw cysts are in The image is often unclear. When directly using edge detection to detect the cyst boundary, it is necessary to continuously adjust the parameters of the high and low thresholds in the detection algorithm to ensure that clear edges can be obtained. Therefore, it lacks adaptability, resulting in the technical problem of unsatisfactory image enhancement effect. The purpose of the present invention is to provide a method and system for enhancing medical images of jaw cysts based on image processing. The specific technical solutions adopted are as follows: A method for enhancing medical images of jaw cysts based on image processing, the method includes: obtaining the jaw CT image of the patient; obtaining the gray histogram of the gray values of all pixel points in the jaw CT image; clustering the gray values of all pixel points according to the gray value distribution in the gray histogram to obtain all gray clusters; obtaining the tissue cluster gray value and the non-tissue cluster gray value according to the gray distribution in all the gray clusters and the number of pixel points corresponding to each gray value; obtaining the edge coefficient of each pixel point according to the gray difference feature within the preset neighborhood of each pixel point in the jaw CT image and the gray difference between the gray values of other pixel points within the preset neighborhood and the tissue cluster gray value and the non-tissue cluster gray value; connecting the pixel points according to the edge coefficient difference between each pixel point and other pixel points within the preset neighborhood to obtain the regional dividing line between the tissue area and the non-tissue area in the jaw CT image; enhancing the jaw CT image according to the regional dividing line.

[0005] Further, the method for obtaining all the gray clusters includes: The gray values of the gray histogram are concentrated in three gray intervals; The area composed of the pixel points with the gray value closest to 0 in the gray histogram is used as the background area; The pixel points in the background area are screened out, and the other pixel points in the jaw CT image are clustered, and the number of clusters is set to 2 to obtain 2 gray clusters.

[0006] Further, the method for obtaining the tissue cluster gray value and the non-tissue cluster gray value includes: Taking one of the gray clusters as the reference cluster; Taking the other gray cluster as the comparison cluster; Obtaining the tissue cluster gray value and the non-tissue cluster gray value according to the calculation formula of the tissue cluster gray value and the non-tissue cluster gray value. The calculation formula of the tissue cluster gray value and the non-tissue cluster gray value is as follows: ; In the formula, represents the tissue cluster gray value; represents the number of pixel points in the reference cluster; represents the number of pixel points corresponding to the th gray value in the reference cluster; represents the th gray value in the reference cluster; represents the number of pixel points in the comparison cluster; represents the number of pixel points corresponding to the th gray value in the comparison cluster; represents the th gray value in the comparison cluster; represents the gray level of the non - tissue cluster; represents the minimum value function; represents the maximum value function.

[0007] Furthermore, the method for obtaining the edge coefficient includes: randomly selecting a pixel point in the jaw CT image as a reference pixel point; obtaining the number of tissue - gray pixel points and the number of non - tissue - gray pixel points in the preset neighborhood of the reference pixel point according to the gray - level differences between the gray values of other pixel points in the preset neighborhood of the reference pixel point, the tissue - cluster gray level, and the non - tissue - cluster gray level; obtaining the edge coefficient according to the edge - coefficient calculation formula, and the edge - coefficient calculation formula is as follows: In the formula, represents the number of tissue - gray pixel points in the preset neighborhood of the reference pixel point; represents the number of non - tissue - gray pixel points in the preset neighborhood of the reference pixel point; represents the number of other pixel points in the preset neighborhood of the reference pixel point; represents the edge coefficient of the reference pixel point; represents the maximum value of the pixel - point gray levels in the preset neighborhood of the reference pixel point; represents the th gray value of other pixel points in the preset neighborhood of the reference pixel point; represents the exponential function with the natural constant as the base.

[0008] Furthermore, the method for obtaining the number of tissue - gray pixel points and the number of non - tissue - gray pixel points in the preset neighborhood of the reference pixel point includes: calculating the gray - level differences between the gray values of each other pixel point in the preset neighborhood of the reference pixel point and the tissue - cluster gray level and the non - tissue - cluster gray level, respectively obtaining the tissue - gray - level difference and the non - tissue - gray - level difference; comparing the tissue - gray - level difference and the non - tissue - gray - level difference of each other pixel point, taking the other pixel points with the tissue - gray - level difference greater than the non - tissue - gray - level difference as tissue - gray pixel points, and taking the other pixel points with the tissue - gray - level difference less than the non - tissue - gray - level difference as non - tissue - gray pixel points; counting the number of tissue - gray pixel points and the number of non - tissue - gray pixel points in the preset neighborhood of the reference pixel point.

[0009] Further, the method for obtaining the region dividing line includes: obtaining the connectable pixels of the reference pixel in the preset neighborhood according to the edge similarity feature between the reference pixel and each other pixel in the preset neighborhood; connecting the reference pixel with each connectable pixel to obtain the dividing line of the reference pixel in the preset neighborhood; traversing all pixels to obtain the dividing line of each pixel in the preset neighborhood, and splicing all the dividing lines to obtain the region dividing line between the tissue region and the non-tissue region in the jaw CT image.

[0010] Further, obtaining the connectable pixels of the reference pixel in the preset neighborhood according to the edge similarity feature between the reference pixel and each other pixel in the preset neighborhood includes: calculating the edge coefficient difference between the reference pixel and each other pixel in the preset neighborhood as the first difference; when the ratio of the first difference to the edge coefficient of the reference pixel is not greater than a preset first threshold, taking the other pixels in the corresponding preset neighborhood as the connectable pixels of the reference pixel in the preset neighborhood.

[0011] A jaw cyst medical image enhancement system based on image processing, the system includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a jaw cyst medical image enhancement method based on image processing as described above are implemented.

[0012] A computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the steps of a jaw cyst medical image enhancement method based on image processing as described above are implemented.

[0013] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of a jaw cyst medical image enhancement method based on image processing as described above are implemented.

[0014] The present invention has the following beneficial effects: The present invention collects the jaw CT images of a patient and enhances the jaw CT images, enabling relevant personnel to accurately identify the cyst area in the patient's jaw region; since the degree histogram can reflect the concentration of gray values in the jaw CT image, and the jaw CT image can be divided into a background region, a non-tissue region, and a tissue region according to the concentration of gray values, so first obtain the gray histogram of the gray values of all pixel points in the jaw CT image, and divide the pixel points into two gray clusters according to the gray distribution of the gray histogram; quantify the overall gray values of the two gray clusters to reflect the approximate gray levels in the tissue region and the non-tissue region, so as to facilitate subsequent determination of whether the pixel points in the jaw CT image tend to be edge pixel points; therefore, analyze the gray level of the tissue cluster and the gray level of the non-tissue cluster; compared with the pixel points inside different regions, the gray distribution of the pixel points on the region edge has a higher gray contrast, and the pixel points with more obvious gray contrast are more likely to be located on the region edge, so obtain the edge coefficient of each pixel point according to the gray difference feature within the preset neighborhood of each pixel point in the jaw CT image and the gray difference between the gray values of other pixel points in the preset neighborhood and the gray levels of the tissue cluster and the non-tissue cluster; connect the pixel points through the edge coefficient to obtain a region segmentation line; enhance the jaw CT image according to the region segmentation line. The present invention can obtain clear edges in the jaw CT image to facilitate relevant personnel to determine the specific location of the jaw cyst. 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 following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 Flowchart of a method for enhancing medical images of jaw cysts based on image processing provided by an embodiment of the present invention; Figure 2 Conventional edge detection image of a jaw CT image provided by an embodiment of the present invention; Figure 3 Effect diagram of the region segmentation line of a jaw CT image provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following specifically describes, with reference to the accompanying drawings and preferred embodiments, a method and system for enhancing medical images of jaw cysts based on image processing according to the present invention, including its specific implementation manner, structure, features and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0019] The following specifically describes, with reference to the accompanying drawings, the specific solution of a method and system for enhancing medical images of jaw cysts based on image processing provided by the present invention.

[0020] Please refer to Figure 1 , which shows a method for enhancing medical images of jaw cysts based on image processing provided by an embodiment of the present invention. The method includes: Step S1: Obtain the jaw CT image of the patient.

[0021] The embodiments of the present invention are mainly applied to the cyst recognition scenario in jaw CT images. Therefore, in the embodiments of the present invention, the jaw CT image of the patient is collected and enhanced so that relevant personnel can accurately identify the cyst area in the patient's jaw.

[0022] In the embodiments of the present invention, the patient needs to sit on a chair in front of a CBCT scanner, keep the head centered and try not to move. Usually, it is required that the patient's head is slightly tilted upward or forward to ensure that the jaw area can be clearly displayed. Select the scanning range and parameters, such as resolution, scanning time, etc. When starting the scan, the patient remains still, and the CBCT device starts to rotate and simultaneously takes a series of ray images, and stores the captured images to obtain the initial jaw CT image of the patient.

[0023] Perform gray-scale processing and denoising operations on the jaw CT image to obtain the jaw CT image of the patient. It should be noted that both the gray-scale processing and denoising methods are well-known technical means to those skilled in the art and will not be limited and elaborated here.

[0024] Step S2: Obtain the gray histogram of the gray values of all pixel points in the jaw CT image; cluster the gray values of all pixel points according to the gray value distribution in the gray histogram to obtain all gray clusters; obtain the tissue cluster gray and non-tissue cluster gray according to the gray distribution within all gray clusters and the number of pixel points corresponding to each gray value; obtain the edge coefficient of each pixel point according to the gray difference feature within the preset neighborhood of each pixel point in the jaw CT image and the gray difference between the gray values of other pixel points within the preset neighborhood and the tissue cluster gray and non-tissue cluster gray; connect the pixel points according to the edge coefficient difference between each pixel point and other pixel points within the preset neighborhood to obtain the regional dividing line between the tissue area and the non-tissue area in the jaw CT image.

[0025] Observing the jaw CT image, it can be known that the area with the lowest gray value is the background area, the area with the highest gray value is the non-tissue area, and the area with the gray value in the middle of the two is the tissue area. The gray histogram can reflect the concentration of gray values in the jaw CT image. According to the concentration of gray values, the jaw CT image can be divided into the background area, the non-tissue area and the tissue area. Therefore, in the embodiment of the present invention, first obtain the gray histogram of the gray values of all pixel points in the jaw CT image, and divide the pixel points into three gray clusters according to the gray distribution of the gray histogram.

[0026] Preferably, in an embodiment of the present invention, the method for obtaining the gray cluster includes: since the overall gray values of the background area, the tissue area and the non-tissue area in the jaw CT image are each in a relatively small gray interval, the gray values of the gray histogram are concentrated in three gray intervals, and the three gray intervals respectively correspond to the pixel point gray values of the background area, the pixel point gray values of the tissue area and the pixel point gray values of the non-tissue area.

[0027] Take the area composed of pixel points with the gray value closest to 0 in the gray histogram as the background area. Since the background area is an interference factor in the embodiment of the present invention, the pixel points in the background area are screened out, and the other pixel points in the jaw CT image are clustered, and the number of clusters is set to 2 to obtain 2 gray clusters. The cluster with the relatively small overall gray value in the 2 gray clusters corresponds to the pixel point gray value of the tissue area, and the cluster with the relatively large overall gray value corresponds to the pixel point gray value of the non-tissue area. It should be noted that in the embodiment of the present invention, the K-means clustering algorithm is used to cluster the types of gray values except for the gray values in the background area. This algorithm is a well-known technical means for those skilled in the art and will not be limited and described in detail here.

[0028] Quantify the overall gray values of the two gray clusters to reflect the approximate gray levels within the tissue area and the non-tissue area, so as to facilitate subsequent determination of whether the pixel points in the jaw CT image tend to be edge pixel points.

[0029] Preferably, in the embodiments of the present invention, the method for obtaining the tissue cluster gray level and the non-tissue cluster gray level includes: taking one of the gray level clusters as a reference cluster; taking the other gray level cluster as a comparison cluster.

[0030] Obtain the tissue cluster gray level and the non-tissue cluster gray level according to the calculation formula of the tissue cluster gray level and the non-tissue cluster gray level. The calculation formula of the tissue cluster gray level and the non-tissue cluster gray level is as follows: ; In the formula, represents the tissue cluster gray level; represents the number of pixel points of the reference cluster; represents the number of pixel points corresponding to the th gray level value in the reference cluster; represents the th gray level value in the reference cluster; represents the number of pixel points of the comparison cluster; represents the number of pixel points corresponding to the th gray level value in the comparison cluster; represents the th gray level value in the comparison cluster; represents the non-tissue cluster gray level; represents the minimum value function; represents the maximum value function.

[0031] In the calculation formula of the tissue cluster gray level and the non-tissue cluster gray level, the more the number of pixel points corresponding to each gray level value in the gray level cluster, the more important the gray level value is. At this time, and as weights will also be larger. Perform weighted averaging on the pixel points corresponding to each gray level value in the gray level cluster to obtain and , which are used to represent the overall gray level value of the gray level cluster. Among them, the smaller one is the tissue cluster gray level, and the larger one is the non-tissue cluster gray level.

[0032] Compared with the pixel points inside different regions, the gray level distribution of the pixel points on the region edge has a higher gray level contrast. In the embodiments of the present invention, this contrast relationship is quantified. The more obvious the gray level contrast of the pixel points, the more likely they are to be the pixel points located on the region edge. Therefore, in the embodiments of the present invention, according to the gray level difference characteristics within the preset neighborhood of each pixel point in the jawbone CT image, and the gray level differences between the gray level values of other pixel points in the preset neighborhood and the tissue cluster gray level and the non-tissue cluster gray level, the edge coefficient of each pixel point is obtained.

[0033] Preferably, in the embodiments of the present invention, the method for obtaining the edge coefficient includes: arbitrarily selecting a pixel point in the jawbone CT image as a reference pixel point; since the embodiments of the present invention only study the pixel points in the tissue region and the non-tissue region, the gray levels between the gray levels of each other pixel point in the preset neighborhood of the reference pixel point and the tissue cluster gray level and the non-tissue cluster gray level are calculated, respectively obtaining the tissue gray level difference and the non-tissue gray level difference; in the embodiments of the present invention, the preset neighborhood is set to be a rectangular area centered on the reference pixel point, which needs to be noted that the preset neighborhood can be set by itself and is not limited herein.

[0034] Compare the tissue gray level difference and the non-tissue gray level difference of each other pixel point, and take the other pixel points with the tissue gray level difference greater than the non-tissue gray level difference as tissue gray level pixel points, and take the other pixel points with the tissue gray level difference less than the non-tissue gray level difference as non-tissue gray level pixel points; count the number of tissue gray level pixel points and the number of non-tissue gray level pixel points in the preset neighborhood of the reference pixel point.

[0035] Obtain the edge coefficient according to the edge coefficient calculation formula, and the edge coefficient calculation formula is as follows: In the formula, represents the number of tissue gray level pixel points in the preset neighborhood of the reference pixel point; represents the number of non-tissue gray level pixel points in the preset neighborhood of the reference pixel point; represents the number of other pixel points in the preset neighborhood of the reference pixel point. In the embodiments of the present invention, is 8; represents the edge coefficient of the reference pixel point; represents the maximum gray level of the pixel points in the preset neighborhood of the reference pixel point; represents the gray level of the th other pixel point in the preset neighborhood of the reference pixel point; represents the exponential function with the natural constant as the base.

[0036] In the edge coefficient calculation formula, the sum of the number of tissue gray level pixel points and the number of non-tissue gray level pixel points is the number of other pixel points in the preset neighborhood; the greater the difference between the maximum gray level of the pixel points in the preset neighborhood and the gray level mean value, the more the gray levels of the pixel points in the preset neighborhood of the reference pixel point tend to have an obvious gray level contrast, and at this time the edge coefficient of the reference pixel point is stronger; the larger the value of any one of the number of tissue gray level pixel points and the number of non-tissue gray level pixel points, the more pixel points with a single gray level tendency exist, that is, the more likely the reference pixel point belongs to the pixel points inside a certain region, that is, the larger it is, and at this time the value range of the function in [1,7] is getting smaller and smaller, and the edge coefficient of the reference pixel point is smaller.

[0037] Connect pixel points through edge coefficients to obtain the regional segmentation line.

[0038] Preferably, in the embodiments of the present invention, the method for obtaining the regional separation line includes: calculating the edge coefficient difference between the reference pixel point and each other pixel point in the preset neighborhood as the first difference; when the ratio between the first difference and the edge coefficient of the reference pixel point is not greater than the preset first threshold, taking the other pixel points in the corresponding preset neighborhood as the connectable pixel points of the reference pixel point in the preset neighborhood. In the embodiments of the present invention, the preset first threshold is set to 0.9. It should be noted that the preset first threshold can be set by oneself and is not limited herein.

[0039] Connect the reference pixel point with each connectable pixel point to obtain the separation line of the reference pixel point in the preset neighborhood; traverse all pixel points to obtain the separation line of each pixel point in the preset neighborhood, and splice all the separation lines to obtain the regional separation line between the tissue area and the non-tissue area in the jaw CT image.

[0040] Use the regional segmentation line to replace the edge demarcation line obtained by conventional edge detection.

[0041] Step S3: Enhance the jaw CT image according to the regional separation line.

[0042] The result directly obtained by using the traditional edge detection algorithm is as Figure 2 shown in a conventional edge detection image of a jaw CT image. It can be observed that the edges of some tissues or non-tissues are not well identified, especially the edges of the cyst part are not detected. Through the processing of the embodiments of the present invention, it is possible to obtain Figure 3 the effect diagram of the regional segmentation line of the jaw CT image shown. It can be observed that the identification of the tissue and non-tissue edges is significantly clearer compared with the traditional edge detection algorithm, and the edges of the key positions where the cysts are located are also detected.

[0043] Thus, the enhancement of the jaw CT image is completed.

[0044] In summary, obtain the jaw CT image of the patient; obtain the gray histogram of the gray values of all pixel points in the jaw CT image; cluster the gray values of all pixel points according to the gray value distribution in the gray histogram to obtain all gray clusters; obtain the tissue cluster gray and non-tissue cluster gray according to the gray distribution within all gray clusters and the number of pixel points corresponding to each gray value; obtain the edge coefficient of each pixel point according to the gray difference feature within the preset neighborhood of each pixel point in the jaw CT image and the gray difference between the gray values of other pixel points within the preset neighborhood and the tissue cluster gray and non-tissue cluster gray; connect the pixel points according to the edge coefficient difference between each pixel point and other pixel points within the preset neighborhood to obtain the regional dividing line between the tissue area and the non-tissue area in the jaw CT image; enhance the jaw CT image according to the regional dividing line.

[0045] An embodiment of the present invention provides a medical image enhancement system for jaw cysts based on image processing. The system includes a memory, a processor, and a computer program. The memory is used to store the corresponding computer program, and the processor is used to run the corresponding computer program. When the computer program runs in the processor, it can implement the methods described in steps S1 - S3.

[0046] The third object of the embodiment of the present invention is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the methods described in steps S1 - S3.

[0047] The fourth object of the embodiment of the present invention is to provide a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by the processor, it implements the methods described in steps S1 - S3.

[0048] It should be noted that: the above sequence of the embodiments of the present invention is only for description and does not represent the advantages or disadvantages of the embodiments. The processes depicted in the 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.

[0049] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

Claims

1. A medical image enhancement method for jaw cysts based on image processing, characterized in that, The method includes: obtaining the jaw CT image of the patient; obtaining the gray histogram of the gray values of all pixel points in the jaw CT image; clustering the gray values of all pixel points according to the gray value distribution in the gray histogram to obtain all gray clusters; obtaining the tissue cluster gray and non-tissue cluster gray according to the gray distribution within all the gray clusters and the number of pixel points corresponding to each gray value; obtaining the edge coefficient of each pixel point according to the gray difference feature within the preset neighborhood of each pixel point in the jaw CT image and the gray difference between the gray values of other pixel points within the preset neighborhood and the tissue cluster gray and non-tissue cluster gray; connecting the pixel points according to the edge coefficient difference between each pixel point and other pixel points within the preset neighborhood to obtain the regional dividing line between the tissue area and the non-tissue area in the jaw CT image; enhancing the jaw CT image according to the regional dividing line; the method for obtaining the tissue cluster gray and non-tissue cluster gray includes: taking one of the gray clusters as the reference cluster; taking the other gray cluster as the comparison cluster; obtaining the tissue cluster gray and non-tissue cluster gray according to the calculation formula of the tissue cluster gray and non-tissue cluster gray, and the calculation formula of the tissue cluster gray and non-tissue cluster gray is as follows: ; In the formula, represents the tissue cluster gray; represents the number of pixel points in the reference cluster; represents the number of pixel points corresponding to the th gray value in the reference cluster; represents the number of pixel points corresponding to the th gray value in the reference cluster; represents the number of pixel points in the comparison cluster; represents the number of pixel points corresponding to the th gray value in the comparison cluster; represents the number of pixel points corresponding to the th gray value in the comparison cluster; represents the non-tissue cluster gray; represents the minimum value function; represents the maximum value function; the method for obtaining the edge coefficient includes: randomly selecting a pixel point in the jaw CT image as the reference pixel point; obtaining the number of tissue gray pixel points and non-tissue gray pixel points within the preset neighborhood of the reference pixel point according to the gray difference between the gray values of other pixel points within the preset neighborhood of the reference pixel point and the tissue cluster gray and non-tissue cluster gray; obtaining the edge coefficient according to the edge coefficient calculation formula, and the edge coefficient calculation formula is as follows: In the formula, represents the number of tissue gray pixel points within the preset neighborhood of the reference pixel point; Represents the number of non-tissue gray pixels within the preset neighborhood of the reference pixel; Represents the number of other pixels within the preset neighborhood of the reference pixel; Represents the edge coefficient of the reference pixel; Represents the maximum gray value of the pixels within the preset neighborhood of the reference pixel; Represents the gray value of the Represents the exponential function with the natural constant as the base.

2. The medical image enhancement method for jaw cyst based on image processing according to claim 1, characterized in that The method for obtaining all the gray-level clusters includes: the gray-level values of the gray-level histogram are concentrated in three gray-level intervals; the area composed of the pixel points with the gray-level value closest to 0 in the gray-level histogram is used as the background area; the pixel points in the background area are screened out, and the other pixel points in the jaw CT image are clustered, with the number of clusters set to 2, and 2 gray-level clusters are obtained.

3. A method for enhancing medical images of jaw cysts based on image processing according to claim 1, characterized in that, The method for obtaining the number of tissue gray-level pixel points and non-tissue gray-level pixel points within the preset neighborhood of the reference pixel point includes: calculating the gray-level between the gray-level value of each other pixel point within the preset neighborhood of the reference pixel point and the tissue cluster gray-level and non-tissue cluster gray-level, respectively obtaining the tissue gray-level difference and the non-tissue gray-level difference; comparing the tissue gray-level difference and the non-tissue gray-level difference of each other pixel point, taking the other pixel points with the tissue gray-level difference greater than the non-tissue gray-level difference as tissue gray-level pixel points, and taking the other pixel points with the tissue gray-level difference less than the non-tissue gray-level difference as non-tissue gray-level pixel points; counting the number of tissue gray-level pixel points and non-tissue gray-level pixel points within the preset neighborhood of the reference pixel point.

4. A method for enhancing medical images of jaw cysts based on image processing according to claim 1, characterized in that, The method for obtaining the regional separation line includes: obtaining the connectable pixel points of the reference pixel point within the preset neighborhood according to the edge similarity feature between the reference pixel point and each other pixel point within the preset neighborhood; connecting the reference pixel point with each connectable pixel point to obtain the separation line of the reference pixel point within the preset neighborhood; traversing all pixel points to obtain the separation line of each pixel point within the preset neighborhood, and splicing all the separation lines to obtain the regional separation line between the tissue area and the non-tissue area in the jaw CT image.

5. A method for enhancing medical images of jaw cysts based on image processing according to claim 4, characterized in that, According to the edge similarity feature between the reference pixel point and each other pixel point within the preset neighborhood, Obtaining the connectable pixel points of the reference pixel point within the preset neighborhood includes: calculating the edge coefficient difference between the reference pixel point and each other pixel point within the preset neighborhood as the first difference; when the ratio of the first difference to the edge coefficient of the reference pixel point is not greater than the preset first threshold, taking the other pixel points within the corresponding preset neighborhood as the connectable pixel points of the reference pixel point within the preset neighborhood.

6. A medical image enhancement system for jaw cysts based on image processing, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a method for enhancing medical images of jaw cysts based on image processing according to any one of claims 1 to 5.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of a method for enhancing medical images of jaw cysts based on image processing according to any one of claims 1 to 5.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a method for enhancing medical images of jaw cysts based on image processing according to any one of claims 1 to 5.

Images