A method, device, equipment and storage medium for calculating dual-energy subtraction parameters

Abstract

The present application provides a method for calculating dual-energy subtraction parameters, which is applied to calculate the soft tissue subtraction parameters and bone subtraction parameters of the shadow area in the dual-energy X-ray medical image, wherein the dual-energy X-ray medical image includes a high-energy image and a low-energy image, and includes the steps of: obtaining a target dual-energy X-ray medical image for registration processing; determining the area to be tested based on the position of the maximum value of the gradient amplitude image of the preset area, determining the subtraction image based on the high-energy and low-energy sub-image blocks, generating the relative standard deviation corresponding to each pixel in the area to be tested based on the standard deviation and mean of the subtraction image; determining the soft tissue subtraction parameters and bone subtraction parameters based on the minimum value of the relative standard deviation. The region of interest of the present application, i.e., the area to be tested, is much smaller than the image size, the algorithm is highly efficient, and the relative standard deviation is used as the judgment criterion based on the visibility of the bone edge, and the effect is good, which can basically meet the clinical requirements at one time without manual fine-tuning.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a method, device, equipment and storage medium for calculating dual-energy subtraction parameters. Background Art

[0002] Nowadays, with the development and popularization of medical imaging technology, dual-energy X-ray photography technology has been more and more widely used. Dual-Energy Subtraction is based on the different attenuation characteristics of human tissues to high and low energy X-rays, based on the X-ray attenuation physical model, and uses digital image processing technology to achieve the image separation of human soft tissue and bones. Compared with conventional X-rays, dual-energy X-rays take longer, and the subtraction results are often interfered by various factors, resulting in the inability to guarantee the accuracy of subtraction, which limits the clinical popularization of this technology to a certain extent. In dual-energy X-rays, image post-processing is the main time-consuming link, among which the search for subtraction parameters is an important reason for the low processing efficiency.

[0003] However, the similar related patents found in the search are "Method and Apparatus to Automatically Determine Tissue Cancellation Parameters in X-Ray Dual Energy Imaging", applicant: GE Medical Systems Global Technology Company. The main idea of ​​this patent solution is to minimize the subtraction image gradient within the preset subtraction parameter range to obtain the soft tissue subtraction parameters, and then determine the bone subtraction parameters based on the empirical relationship between the soft tissue and bone subtraction parameters. This solution does not exclude non-tissue areas in the gradient calculation, which causes the information of these areas to interfere and affect the accuracy of the calculation results. In addition, although the image has been reduced several times in this solution, the efficiency is low because the optimal subtraction parameter statistics need to be performed for each pixel. "Estimation Method and Computer-readable Storage Medium for Dual Energy Subtraction Parameters", applicant: Shenzhen Anjian Technology Co., Ltd. The main idea of ​​this patent solution is to determine the bone subtraction parameters by changing the subtraction parameters, minimizing the variance of the soft tissue point set, and minimizing the grayscale difference between the soft tissue point set and the bone tissue point set to determine the soft tissue subtraction parameters. This method uses absolute grayscale values ​​in both variance and grayscale difference calculations. However, the grayscale values ​​of dual-energy subtraction images have normalization problems, and using absolute grayscale values ​​has a greater risk of error. In addition, the optimal soft tissue subtraction parameters are determined based on minimizing the grayscale difference between the bone point set and the soft tissue point set. From the experimental results, the effect is not very ideal, and there is a significant difference between the numerical calculation results and the visual intuitive results.

[0004] At present, there are two main search methods for dual-energy subtraction parameters: manual search and computer automatic search. One is manual search, that is, the user manually adjusts the subtraction parameters and reads the film in real time until the best subtraction effect is obtained, and the best subtraction parameters are determined at the same time. The manual search method is simple to implement and has high accuracy, but the operation is cumbersome, time-consuming and labor-intensive, and the clinical experience is not good. The other is computer automatic search, which is performed completely automatically by the computer according to certain criteria, and the search is carried out continuously until the best subtraction parameters are obtained. The computer automatic method does not require manual intervention, saves time and effort, and is more conducive to clinical application. However, the commonly used search methods currently have the problem of insufficient precision. There is a significant difference between the numerical calculation results and the visual intuitive results. The search results are often not very ideal, and manual fine-tuning is often required to obtain the final accurate results. Summary of the invention

[0005] In view of the above problems, the present application is proposed to provide a method, device, apparatus and storage medium for calculating dual-energy subtraction parameters that overcome the above problems or at least partially solve the above problems.

[0006] In order to solve the above problems, the present application discloses a method for calculating dual-energy subtraction parameters, which is applied to calculate soft tissue subtraction parameters and bone subtraction parameters of shadow areas in dual-energy X-ray medical images, wherein the dual-energy X-ray medical images include high-energy images and low-energy images, and include the steps of:

[0007] Acquire a target dual-energy X-ray medical image, and perform registration processing on the target high-energy image and the target low-energy image;

[0008] Determine the area to be tested according to the position of the maximum value of the gradient amplitude image of the preset area in the target low-energy image after the registration processing, and determine the high-energy sub-image block corresponding to the area to be tested in the target high-energy image and the low-energy sub-image block corresponding to the area to be tested in the target low-energy image;

[0009] Determine a subtraction image corresponding to each pixel in the area to be tested based on the high-energy sub-image block and the low-energy sub-image block, determine a standard deviation and a mean of the subtraction image, and generate a relative standard deviation corresponding to each pixel in the area to be tested based on the standard deviation and the mean of the subtraction image;

[0010] The soft tissue subtraction parameter and the bone subtraction parameter are determined according to the minimum value of the relative standard deviation.

[0011] Furthermore, the step of determining the area to be tested according to the position of the maximum value of the gradient amplitude image of the preset area in the target low-energy image after the registration processing, and determining the high-energy sub-image block corresponding to the area to be tested in the target high-energy image and the low-energy sub-image block corresponding to the area to be tested in the target low-energy image includes:

[0012] Determine the position of a preset area in the target low-energy image after the registration process, and generate the gradient amplitude image based on pixels in the preset area;

[0013] Obtaining the position of the maximum value of the gradient amplitude image, and converting the position of the maximum value of the gradient amplitude image according to the position of the preset area in the target low-energy image to determine the target position, and setting the neighborhood position of the target position as the area to be measured;

[0014] According to the position of the area to be tested, a high-energy sub-image block corresponding to the area to be tested in the target high-energy image and a low-energy sub-image block corresponding to the area to be tested in the target low-energy image are determined.

[0015] Furthermore, the step of determining the position of a preset area in the target low-energy image after the registration process, and generating the gradient amplitude image according to pixels in the preset area, comprises:

[0016] Determining the position of a preset area in the target low-energy image after the registration process;

[0017] Performing low-pass filtering on pixels in a preset area of ​​the target low-energy image;

[0018] The gradient magnitude image is generated according to the pixels in the preset area after low-pass filtering.

[0019] Furthermore, the step of generating the gradient amplitude image based on the pixels in the preset area after low-pass filtering includes:

[0020] Determine the x component and y component of the pixel gradient in the preset area after low-pass filtering;

[0021] The gradient magnitude image is generated according to the x component and the y component of the pixel gradient in the preset area.

[0022] Furthermore, the step of determining the subtraction image corresponding to each pixel in the area to be tested based on the high-energy sub-image block and the low-energy sub-image block, determining the standard deviation and mean of the subtraction image, and generating the relative standard deviation corresponding to each pixel in the area to be tested based on the standard deviation and mean of the subtraction image includes:

[0023] Determine the subtraction image corresponding to each pixel in the area to be tested based on the difference in energy attenuation mode and photoelectric absorption effect of bone and soft tissue on X-ray photons and the ratio of the high-energy sub-image block to the low-energy sub-image block corresponding to each pixel in the area to be tested;

[0024] Determine the standard deviation and mean of the subtraction image corresponding to each pixel in the area to be tested;

[0025] The relative standard deviation corresponding to each pixel in the area to be measured is generated according to the ratio of the standard deviation to the mean of the subtraction image corresponding to each pixel in the area to be measured.

[0026] Furthermore, the step of determining the soft tissue subtraction parameter according to the minimum value of the relative standard deviation, and determining the bone subtraction parameter according to the soft tissue subtraction parameter comprises:

[0027] Determine the subtraction parameter corresponding to the minimum value of the relative standard deviation as the soft tissue subtraction parameter;

[0028] The bone subtraction parameters are determined according to the soft tissue subtraction parameters and the preset weights.

[0029] Furthermore, the step of determining the bone subtraction parameters according to the soft tissue subtraction parameters and preset weights includes:

[0030] The bone subtraction parameter is determined according to the sum of the soft tissue subtraction parameter and the preset weight; wherein the value of the preset weight is 0.25.

[0031] A dual-energy subtraction parameter calculation device is used to calculate soft tissue subtraction parameters and bone subtraction parameters of shadow areas in dual-energy X-ray medical images, wherein the dual-energy X-ray medical images include high-energy images and low-energy images, including:

[0032] A registration module is used to acquire a target dual-energy X-ray medical image and perform registration processing on a target high-energy image and a target low-energy image;

[0033] A determination module, used to determine the area to be tested according to the position of the maximum value of the gradient amplitude image of the preset area in the target low-energy image after the registration process, and determine the high-energy sub-image block corresponding to the area to be tested in the target high-energy image and the low-energy sub-image block corresponding to the area to be tested in the target low-energy image;

[0034] A calculation module, used to determine the subtraction image corresponding to each pixel in the area to be measured based on the high-energy sub-image block and the low-energy sub-image block, and determine the standard deviation and mean of the subtraction image, and generate the relative standard deviation corresponding to each pixel in the area to be measured based on the standard deviation and mean of the subtraction image;

[0035] An output module is used to determine the soft tissue subtraction parameter and the bone subtraction parameter according to the minimum value of the relative standard deviation.

[0036] A computer device comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the steps of the method for calculating dual-energy subtraction parameters as described above are implemented.

[0037] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for calculating dual-energy subtraction parameters as described above are implemented.

[0038] This application has the following advantages:

[0039] In an embodiment of the present application, a target dual-energy X-ray medical image is acquired, and a target high-energy image and a target low-energy image are registered; a region to be tested is determined based on the position of the maximum value of a gradient amplitude image of a preset region in the target low-energy image after the registration process, and a high-energy sub-image block corresponding to the region to be tested in the target high-energy image and a low-energy sub-image block corresponding to the region to be tested in the target low-energy image are determined; a subtraction image corresponding to each pixel in the region to be tested is determined based on the high-energy sub-image block and the low-energy sub-image block, and the standard deviation and mean of the subtraction image are determined, and a relative standard deviation corresponding to each pixel in the region to be tested is generated based on the standard deviation and mean of the subtraction image; soft tissue subtraction parameters and bone subtraction parameters are determined based on the minimum value of the relative standard deviation. The present application extracts a rectangular area of ​​a preset size containing the bone edge from the low-energy image, takes it as the region of interest (ROI), that is, the area to be tested, calculates the relative standard deviation of the ROI area in the subtraction image under different pixels, takes the subtraction parameter corresponding to the minimum relative standard deviation as the soft tissue subtraction parameter, and then determines the bone subtraction parameter based on the empirical relationship between the bone and soft tissue subtraction parameters; the determination of the ROI of the present application does not involve complex feature extraction, and the image ROI actually used for calculation is much smaller than the image size, which greatly improves the efficiency of the algorithm. In principle, the algorithm has good robustness and low computational overhead, is easy to implement, and provides a guarantee for clinical rapid acquisition of accurate dual-energy subtraction results, and lays a foundation for further imaging diagnosis; in addition, the relative standard deviation curve usually has a good convex property, which ensures that the search algorithm can obtain a relatively stable optimal value, and the relative standard deviation is based on the bone edge visibility as the judgment criterion. The judgment result is more in line with the intuitive result of the human eye, the effect is relatively good, and it can basically meet the clinical requirements at one time without manual fine-tuning. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the description of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 is a flowchart of a method for calculating dual-energy subtraction parameters provided in one embodiment of the present application;

[0042] Figure 2 is a flowchart of a method for calculating dual-energy subtraction parameters provided in one embodiment of the present application;

[0043] Figure 3 It is a schematic diagram of a relative standard deviation curve of a method for calculating dual-energy subtraction parameters provided in an embodiment of the present application;

[0044] Figure 4 This is a rendering of a method for calculating dual-energy subtraction parameters provided in one embodiment of the present application;

[0045] Figure 5 It is a structural block diagram of a dual-energy subtraction parameter calculation device provided by an embodiment of the present application;

[0046] Figure 6 It is a structural schematic diagram of a computer device for a method for calculating dual-energy subtraction parameters provided in one embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the objects, features and advantages of the present application more obvious and understandable, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0048] The core concept of the present application is to obtain a target dual-energy X-ray medical image and perform registration processing on the target high-energy image and the target low-energy image; determine the area to be tested based on the position of the maximum value of the gradient amplitude image of the preset area in the target low-energy image after the registration processing, and determine the high-energy sub-image block corresponding to the area to be tested in the target high-energy image and the low-energy sub-image block corresponding to the area to be tested in the target low-energy image; determine the subtraction image corresponding to each pixel in the area to be tested based on the high-energy sub-image block and the low-energy sub-image block, and determine the standard deviation and mean of the subtraction image, and generate the relative standard deviation corresponding to each pixel in the area to be tested based on the standard deviation and mean of the subtraction image; determine the soft tissue subtraction parameters and the bone subtraction parameters based on the minimum value of the relative standard deviation.

[0049] It should be noted that in any embodiment of the present application, the digital image is composed of two-dimensional elements, each of which has a specific position (x, y) and amplitude f(x, y), and these elements are called pixels. An edge refers to a set of connected pixels, and the grayscale around these pixels has a significant change. The grayscale profile of this part can generally be regarded as a step, that is, a sharp change from one grayscale value in a very small buffer area to another grayscale value with a large grayscale difference. The edge of the image concentrates most of the information of the image. The edge is where the pixel value changes rapidly, and it contains the main information of the target object in the image.

[0050] Reference Figure 1-2 , shows a flowchart of a method for calculating dual-energy subtraction parameters provided by an embodiment of the present application, which is applied to calculate soft tissue subtraction parameters and bone subtraction parameters of shadow areas in dual-energy X-ray medical images, wherein the dual-energy X-ray medical images include high-energy images and low-energy images; comprising the steps of:

[0051] S110, acquiring a target dual-energy X-ray medical image, and performing registration processing on the target high-energy image and the target low-energy image;

[0052] S120, determining the area to be tested according to the position of the maximum value of the gradient amplitude image of the preset area in the target low-energy image after the registration process, and determining the high-energy sub-image block corresponding to the area to be tested in the target high-energy image and the low-energy sub-image block corresponding to the area to be tested in the target low-energy image;

[0053] S130, determining a subtraction image corresponding to each pixel in the area to be tested based on the high-energy sub-image block and the low-energy sub-image block, and determining a standard deviation and a mean of the subtraction image, and generating a relative standard deviation corresponding to each pixel in the area to be tested based on the standard deviation and the mean of the subtraction image;

[0054] S140, determining soft tissue subtraction parameters and bone subtraction parameters according to the minimum value of the relative standard deviation.

[0055] In an embodiment of the present application, a target dual-energy X-ray medical image is acquired, and a target high-energy image and a target low-energy image are registered; a region to be tested is determined based on the position of the maximum value of a gradient amplitude image of a preset region in the target low-energy image after the registration process, and a high-energy sub-image block corresponding to the region to be tested in the target high-energy image and a low-energy sub-image block corresponding to the region to be tested in the target low-energy image are determined; a subtraction image corresponding to each pixel in the region to be tested is determined based on the high-energy sub-image block and the low-energy sub-image block, and the standard deviation and mean of the subtraction image are determined, and a relative standard deviation corresponding to each pixel in the region to be tested is generated based on the standard deviation and mean of the subtraction image; soft tissue subtraction parameters and bone subtraction parameters are determined based on the minimum value of the relative standard deviation. The present application extracts a rectangular area of ​​a preset size containing the bone edge from the low-energy image, takes it as the region of interest (ROI), that is, the area to be tested, calculates the relative standard deviation of the ROI area in the subtraction image under different pixels, takes the subtraction parameter corresponding to the minimum relative standard deviation as the soft tissue subtraction parameter, and then determines the bone subtraction parameter based on the empirical relationship between the bone and soft tissue subtraction parameters; the determination of the ROI of the present application does not involve complex feature extraction, and the image ROI actually used for calculation is much smaller than the image size, which greatly improves the efficiency of the algorithm. In principle, the algorithm has good robustness and low computational overhead, is easy to implement, and provides a guarantee for clinical rapid acquisition of accurate dual-energy subtraction results, and lays a foundation for further imaging diagnosis; in addition, the relative standard deviation curve usually has a good convex property, which ensures that the search algorithm can obtain a relatively stable optimal value, and the relative standard deviation is based on the bone edge visibility as the judgment criterion. The judgment result is more in line with the intuitive result of the human eye, the effect is relatively good, and it can basically meet the clinical requirements at one time without manual fine-tuning.

[0056] Next, a calculation method of a dual-energy subtraction parameter in this exemplary embodiment will be further described.

[0057] As described in step S110, a target dual-energy X-ray medical image is acquired, and a registration process is performed on the target high-energy image and the target low-energy image.

[0058] In one embodiment of the present invention, the specific process of "obtaining a target dual-energy X-ray medical image and registering the target high-energy image and the target low-energy image" in step S110 can be further explained in combination with the following description.

[0059] In one embodiment of the present application, a target dual-energy X-ray medical image is acquired, and a registration process is performed on the target high-energy image and the target low-energy image to ensure that the same coordinates in the two images correspond to the same point in the tissue.

[0060] It should be noted that registration refers to the matching of geographic coordinates of different image graphics obtained by different imaging methods in the same area. Image registration is the process of matching and superimposing two or more images acquired at different times, different sensors (imaging devices) or different conditions. The registration process is as follows: first, feature points are extracted from the two images; matching feature point pairs are found by performing similarity measurement; then, image space coordinate transformation parameters are obtained through the matching feature point pairs; and finally, image registration is performed based on the coordinate transformation parameters.

[0061] As described in step S120, the area to be tested is determined based on the position of the maximum value of the gradient amplitude image of the preset area in the target low-energy image after the registration processing, and the high-energy sub-image block corresponding to the area to be tested in the target high-energy image and the low-energy sub-image block corresponding to the area to be tested in the target low-energy image are determined.

[0062] In one embodiment of the present application, the specific process of step S120 of "determining the area to be tested based on the position of the maximum value of the gradient amplitude image of the preset area in the target low-energy image after the registration processing, and determining the high-energy sub-image block corresponding to the area to be tested in the target high-energy image and the low-energy sub-image block corresponding to the area to be tested in the target low-energy image" can be further explained in combination with the following description.

[0063] As described in the following steps, the position of a preset area in the target low-energy image after the registration process is determined, and the gradient amplitude image is generated according to the pixels in the preset area;

[0064] As described in the following steps, the position of the maximum value of the gradient amplitude image is obtained, and the position of the maximum value of the gradient amplitude image is converted according to the position of the preset area in the target low-energy image to determine the target position, and the neighborhood position of the target position is set as the area to be measured;

[0065] As described in the following steps, according to the position of the area to be tested, a high-energy sub-image block corresponding to the area to be tested in the target high-energy image and a low-energy sub-image block corresponding to the area to be tested in the target low-energy image are determined.

[0066] In one embodiment of the present application, the position of a preset area in the target low-energy image after the registration process is determined; the pixels in the preset area in the target low-energy image are subjected to low-pass filtering to eliminate the influence of noise; the x-component and y-component of the pixel gradient in the preset area after the low-pass filtering process are determined; and the gradient amplitude image is generated based on the x-component and y-component of the pixel gradient in the preset area.

[0067] It should be noted that low-pass filtering is a filtering method. The rule is that low-frequency signals can pass normally, while high-frequency signals that exceed the set critical value are blocked and weakened; however, the extent of blocking and weakening will change according to different frequencies and different filtering purposes. In the field of digital image processing, from the frequency domain, low-pass filtering can smooth and reduce noise on images.

[0068] In a specific implementation, according to the radiographic location, a sub-region is extracted from the diagnostic area of ​​the target low-energy image after the registration process to be determined as a preset region R1 (the size of the preset region can be 300*300), and the pixels of the preset region R1 are subjected to low-pass filtering to eliminate the influence of noise; the x component and y component of the pixel gradient in the preset region after the low-pass filtering process are determined; the gradient amplitude image g is generated according to the x component and y component of the pixel gradient in the preset region; the position coordinate p1 of the maximum value of the gradient amplitude image is obtained, and according to the position of the preset region R1 in the target low-energy image, the position coordinate p1 of the maximum value of the gradient amplitude image is converted to the coordinate p2 of the target position in the target low-energy image, and then the 50*50 neighborhood position of the target position is taken as the center with the coordinate p2 of the target position as the center and is set as the region of interest R2, and the region of interest R2 is the region to be tested; according to the position of the region to be tested, the high-energy sub-image block R corresponding to the region to be tested is extracted in the target high-energy image and the target low-energy image respectively. 2H and low energy sub-image block R 2L The calculation formula of the gradient magnitude image g is as follows:

[0069] g=|g x |+|g y |

[0070] Where g x and g y are the x and y components of the gradient, respectively.

[0071] It should be noted that the bone contrast of low-energy images is usually better than that of high-energy images, which is more conducive to edge extraction. Therefore, a sub-region is extracted from the diagnostic area of ​​the target low-energy image after the registration process to be determined as the preset region R1. The position of the preset region R1 can be determined according to the filming site and based on the experience of clinical radiologists; taking the most commonly taken chest anteroposterior film as an example, a sub-region is usually extracted from the upper part of the two lung lobes to be determined as the preset region R1. The original meaning of gradient is a vector (vector), which means that the directional derivative of a function at this point has the maximum value along this direction; the gradient amplitude is the modulus of the gradient, and the image gradient represents the speed of image change, reflecting the edge information of the image. For the edge part of the image, its grayscale value changes greatly and the gradient value is also large; for the smoother part of the image, its grayscale value changes less and the gradient value is also smaller. In order to detect edges, it is necessary to detect discontinuities in the image, and image gradients can be used to detect discontinuities.

[0072] As described in step S130, the subtraction image corresponding to each pixel in the area to be tested is determined based on the high-energy sub-image block and the low-energy sub-image block, and the standard deviation and mean of the subtraction image are determined, and the relative standard deviation corresponding to each pixel in the area to be tested is generated based on the standard deviation and mean of the subtraction image.

[0073] In one embodiment of the present application, the specific process of step S130 of "determining the subtraction image corresponding to each pixel in the area to be tested based on the high-energy sub-image block and the low-energy sub-image block, and determining the standard deviation and mean of the subtraction image, and generating the relative standard deviation corresponding to each pixel in the area to be tested based on the standard deviation and mean of the subtraction image" can be further explained in combination with the following description.

[0074] As described in the following steps, based on the differences in the energy attenuation mode and photoelectric absorption effect of bones and soft tissues on X-ray photons, and in combination with the ratio of the high-energy sub-image block to the low-energy sub-image block corresponding to each pixel in the area to be tested, a subtraction image corresponding to each pixel in the area to be tested is determined;

[0075] As described in the following steps, the standard deviation and mean of the subtraction image corresponding to each pixel in the area to be tested are determined;

[0076] As described in the following steps, the relative standard deviation corresponding to each pixel in the area to be tested is generated according to the ratio of the standard deviation to the mean of the subtraction image corresponding to each pixel in the area to be tested.

[0077] In a specific implementation, according to the difference in energy attenuation mode and photoelectric absorption effect of bone and soft tissue on X-ray photons, and in combination with the high-energy sub-image block R corresponding to each pixel in the area to be tested, 2H and low energy sub-image block R 2LThe ratio of determines the subtraction image R corresponding to each pixel in the test area. DES , determine the subtraction image R corresponding to each pixel in the test area DES The standard deviation STD and mean AVG of each pixel in the test area are calculated based on the subtraction image R corresponding to each pixel in the test area. DES The ratio of the standard deviation STD to the average AVG generates the relative standard deviation relative-std corresponding to each pixel in the test area.

[0078] In one embodiment of the present application, the subtraction image R DES The calculation formula is as follows:

[0079]

[0080] Where x and y are pixel coordinates, and w is the subtraction parameter. Let w change in the interval [0.2, 0.7] with a step size of 0.01, and a subtraction image sequence can be obtained.

[0081] Reference Figure 3 , shows a relative standard deviation curve schematic diagram of a dual-energy subtraction parameter calculation method provided in an embodiment of the present application. According to the relative standard deviation relative-std calculation formula, for each subtraction image R in the subtraction image sequence DES Calculation is performed, each subtraction parameter corresponds to a subtraction image, and thus corresponds to a relative standard deviation, from which a relative standard deviation curve can be obtained. Its relative standard deviation curve usually has a good convex property, ensuring that the search algorithm can obtain a relatively stable optimal value. The calculation formula of relative standard deviation relative-std is as follows:

[0082]

[0083] As described in step S140, the soft tissue subtraction parameter and the bone subtraction parameter are determined according to the minimum value of the relative standard deviation.

[0084] In an embodiment of the present application, the specific process of "determining the soft tissue subtraction parameter and the bone subtraction parameter according to the minimum value of the relative standard deviation" in step S140 can be further explained in combination with the following description.

[0085] As described in the following steps, the subtraction parameter corresponding to the minimum value of the relative standard deviation is determined as the soft tissue subtraction parameter;

[0086] As described in the following steps, the bone subtraction parameters are determined according to the soft tissue subtraction parameters and the preset weights.

[0087] In a specific implementation, according to the relative standard deviation calculated above, the subtraction parameter corresponding to the minimum relative standard deviation is used as the soft tissue subtraction parameter wS , and then determine the preset weight based on a large amount of experimental statistical data, the value of the preset weight is 0.25, based on the soft tissue subtraction parameter w S The sum of the preset weights determines the bone subtraction parameter w B .

[0088] It should be noted that the minimum relative standard deviation reflects the bone edge with the weakest visibility, and its corresponding subtraction result is visually intuitive as a soft tissue image, so the soft tissue subtraction parameter is determined based on the minimum value of the relative standard deviation. S and the bone subtraction parameter w B The relationship is as follows:

[0089] w B =w S +0.25

[0090] According to the above soft tissue subtraction parameters w S and the bone subtraction parameter w B The relationship between the two parameters can be combined with the determined soft tissue subtraction parameters to determine the bone subtraction parameters.

[0091] Reference Figure 4 , shows a rendering of a method for calculating dual-energy subtraction parameters provided by an embodiment of the present application. From left to right are a standard image, a soft tissue image, and a bone image.

[0092] In one embodiment of the present application, the chest AP film is processed according to the method described above to obtain a dual-energy X-ray medical image of the chest AP film, and the high-energy image and the low-energy image are registered to ensure that the same coordinates in the two images correspond to the same point in the tissue; a sub-region is extracted from the diagnostic area of ​​the low-energy image after the registration process to be determined as a preset region R1 (the size of the preset region is 300*300), and the pixels of the preset region R1 are low-pass filtered to eliminate the influence of noise, and the x component and y component of the pixel gradient in the preset region after the low-pass filtering process are determined; based on the pixels in the preset region, The x component and y component of the pixel gradient of the image are used to generate the gradient amplitude image g; the position coordinate p1 of the maximum value of the gradient amplitude image is obtained, and according to the position of the preset area R1 in the low-energy image, the position coordinate p1 of the maximum value of the gradient amplitude image is converted into the coordinate p2 of the target position in the low-energy image, and then the coordinate p2 of the target position is taken as the center, and its 50*50 neighborhood position is set as the region of interest R2, and the region of interest R2 is the region to be tested; according to the position of the region to be tested, the high-energy sub-image block R corresponding to the region to be tested is extracted in the target high-energy image and the target low-energy image respectively. 2H and low energy sub-image block R 2LAccording to the difference in the energy attenuation mode and photoelectric absorption effect of bone and soft tissue on X-ray photons, combined with the high-energy sub-image block R corresponding to each pixel in the test area 2H and low energy sub-image block R 2L The ratio of determines the subtraction image R corresponding to each pixel in the test area. DES , determine the subtraction image R corresponding to each pixel in the test area DES The standard deviation STD and mean AVG of each pixel in the test area are calculated based on the subtraction image R corresponding to each pixel in the test area. DES The ratio of the standard deviation STD to the mean AVG generates the relative standard deviation relative-std corresponding to each pixel in the test area; the subtraction parameter corresponding to the minimum relative standard deviation is 0.43, so the soft tissue subtraction parameter w S is 0.43, and the bone subtraction parameter w B It is 0.68.

[0093] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0094] Reference Figure 5 , shows a structural block diagram of a dual-energy subtraction parameter calculation device provided by an embodiment of the present application; the dual-energy subtraction parameter calculation device is applied to calculate soft tissue subtraction parameters and bone subtraction parameters of shadow areas in dual-energy X-ray medical images, wherein the dual-energy X-ray medical images include high-energy images and low-energy images, specifically including:

[0095] A registration module 510 is used to acquire a target dual-energy X-ray medical image and perform registration processing on the target high-energy image and the target low-energy image;

[0096] A determination module 520 is used to determine the area to be tested according to the position of the maximum value of the gradient amplitude image of the preset area in the target low-energy image after the registration process, and to determine the high-energy sub-image block corresponding to the area to be tested in the target high-energy image and the low-energy sub-image block corresponding to the area to be tested in the target low-energy image;

[0097] A calculation module 530 is used to determine a subtraction image corresponding to each pixel in the area to be tested based on the high-energy sub-image block and the low-energy sub-image block, and determine a standard deviation and a mean of the subtraction image, and generate a relative standard deviation corresponding to each pixel in the area to be tested based on the standard deviation and the mean of the subtraction image;

[0098] The output module 540 is used to determine the soft tissue subtraction parameter and the bone subtraction parameter according to the minimum value of the relative standard deviation.

[0099] In an embodiment of the present application, the determining module 520 includes:

[0100] A gradient amplitude calculation submodule is used to determine the position of a preset area in the target low-energy image after the registration process, and generate the gradient amplitude image according to the pixels in the preset area;

[0101] Determine the submodule of the area to be measured, obtain the position of the maximum value of the gradient amplitude image, and convert the position of the maximum value of the gradient amplitude image according to the position of the preset area in the target low-energy image to determine the target position, and set the neighborhood position of the target position as the area to be measured;

[0102] The sub-image block determination submodule determines, according to the position of the area to be detected, a high-energy sub-image block in the target high-energy image corresponding to the area to be detected and a low-energy sub-image block in the target low-energy image corresponding to the area to be detected.

[0103] In one embodiment of the present application, the submodule for calculating the gradient amplitude includes:

[0104] A submodule for determining a preset region is used to determine the position of a preset region in the target low-energy image after the registration process;

[0105] A low-pass filtering submodule, used for performing low-pass filtering on pixels in a preset area of ​​the target low-energy image;

[0106] The generating submodule is used to generate the gradient amplitude image according to the pixels in the preset area after the low-pass filtering process.

[0107] In one embodiment of the present application, the generating submodule includes:

[0108] A first generating submodule, used to determine the x component and y component of the pixel gradient in the preset area after low-pass filtering;

[0109] The second generating submodule is used to generate the gradient magnitude image according to the x component and the y component of the pixel gradient in the preset area.

[0110] In one embodiment of the present application, the calculation module 530 includes:

[0111] A first calculation submodule is used to determine the subtraction image corresponding to each pixel in the test area according to the difference in energy attenuation mode and photoelectric absorption effect of bone and soft tissue to X-ray photons, and in combination with the ratio of the high-energy sub-image block to the low-energy sub-image block corresponding to each pixel in the test area;

[0112] A second calculation submodule is used to determine the standard deviation and mean of the subtraction image corresponding to each pixel in the area to be measured;

[0113] The third calculation submodule is used to generate a relative standard deviation corresponding to each pixel in the area to be measured according to the ratio of the standard deviation of the subtraction image corresponding to each pixel in the area to be measured to the mean value.

[0114] In an embodiment of the present application, the output module 540 includes:

[0115] A first output submodule, used for determining the subtraction parameter corresponding to the minimum value of the relative standard deviation as the soft tissue subtraction parameter;

[0116] The second output submodule is used to determine the bone subtraction parameters according to the soft tissue subtraction parameters and preset weights.

[0117] In an embodiment of the present application, the second output submodule includes:

[0118] The bone subtraction parameter determination submodule is used to determine the bone subtraction parameter according to the sum of the soft tissue subtraction parameter and a preset weight; wherein the value of the preset weight is 0.25.

[0119] Reference Figure 6 , shows a schematic diagram of the structure of a computer device for calculating a dual-energy subtraction parameter provided by an embodiment of the present application, which may specifically include the following:

[0120] The computer device 12 is in the form of a general-purpose computing device, and the components of the computer device 12 may include but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0121] The bus 18 represents one or more of several types of bus 18 structures, including a memory bus 18 or memory controller, a peripheral bus 18, an accelerated graphics port, a processor or a local bus 18 using any of a variety of bus 18 architectures. These architectures include, by way of example, but are not limited to, an Industry Standard Architecture (ISA) bus 18, a Micro Channel Architecture (MAC) bus 18, an Enhanced ISA bus 18, an Audio Video Electronics Standards Association (VESA) local bus 18, and a Peripheral Component Interconnect (PCI) bus 18.

[0122] The computer device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer device 12, including volatile and non-volatile media, removable and non-removable media.

[0123] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Computer device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write to non-removable, non-volatile magnetic media (commonly referred to as a "hard drive"). Although Figure 4 Not shown, a disk drive for reading and writing to a removable non-volatile disk (such as a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (such as a CD-ROM, DVD-ROM or other optical medium) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42, which are configured to perform the functions of various embodiments of the present invention.

[0124] A program / utility 40 having a set (at least one) of program modules 42 may be stored in, for example, a memory, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules 42, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0125] The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, cameras, etc.), one or more devices that enable an operator to interact with the computer device 12, and / or any device that enables the computer device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the computer device 12 may also communicate with one or more networks (e.g., local area networks (LANs)), wide area networks (WANs), and / or public networks (e.g., the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with the other modules of the computer device 12 via the bus 18. It should be understood that although Figure 3 Not shown, other hardware and / or software modules may be used in conjunction with the computer device 12, including but not limited to: microcode, device drivers, redundant processing units 16, external disk drive arrays, RAID systems, tape drives, and data backup storage systems 34, etc.

[0126] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing a method for adaptively identifying a rectangular frame of a beam spotter provided in an embodiment of the present invention.

[0127] That is, when the processing unit 16 executes the program, the following steps are implemented: acquiring a target dual-energy X-ray medical image, and registering a target high-energy image and a target low-energy image; determining a region to be tested based on the position of the maximum value of a gradient amplitude image of a preset region in the target low-energy image after the registration process, and determining a high-energy sub-image block corresponding to the region to be tested in the target high-energy image and a low-energy sub-image block corresponding to the region to be tested in the target low-energy image; determining a subtraction image corresponding to each pixel in the region to be tested based on the high-energy sub-image block and the low-energy sub-image block, and determining the standard deviation and mean of the subtraction image, and generating a relative standard deviation corresponding to each pixel in the region to be tested based on the standard deviation and mean of the subtraction image; and determining soft tissue subtraction parameters and bone subtraction parameters based on the minimum value of the relative standard deviation. The present application extracts a rectangular area of ​​a preset size containing the bone edge from the low-energy image, takes it as the region of interest (ROI), that is, the area to be tested, calculates the relative standard deviation of the ROI area in the subtraction image under different pixels, takes the subtraction parameter corresponding to the minimum relative standard deviation as the soft tissue subtraction parameter, and then determines the bone subtraction parameter based on the empirical relationship between the bone and soft tissue subtraction parameters; the determination of the ROI of the present application does not involve complex feature extraction, and the image ROI actually used for calculation is much smaller than the image size, which greatly improves the efficiency of the algorithm. In principle, the algorithm has good robustness and low computational overhead, is easy to implement, and provides a guarantee for clinical rapid acquisition of accurate dual-energy subtraction results, and lays a foundation for further imaging diagnosis; in addition, the relative standard deviation curve usually has a good convex property, which ensures that the search algorithm can obtain a relatively stable optimal value, and the relative standard deviation is based on the bone edge visibility as the judgment criterion. The judgment result is more in line with the intuitive result of the human eye, the effect is relatively good, and it can basically meet the clinical requirements at one time without manual fine-tuning.

[0128] In an embodiment of the present application, the present application further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, a method for calculating a dual-energy subtraction parameter as provided in all embodiments of the present application is implemented:

[0129] That is, when the program is executed by the processor, it is implemented as follows: it includes the following steps: acquiring a target dual-energy X-ray medical image, and registering a target high-energy image and a target low-energy image; determining a region to be tested based on the position of the maximum value of a gradient amplitude image of a preset region in the target low-energy image after the registration process, and determining a high-energy sub-image block corresponding to the region to be tested in the target high-energy image and a low-energy sub-image block corresponding to the region to be tested in the target low-energy image; determining a subtraction image corresponding to each pixel in the region to be tested based on the high-energy sub-image block and the low-energy sub-image block, and determining the standard deviation and mean of the subtraction image, and generating a relative standard deviation corresponding to each pixel in the region to be tested based on the standard deviation and mean of the subtraction image; and determining soft tissue subtraction parameters and bone subtraction parameters based on the minimum value of the relative standard deviation. The present application extracts a rectangular area of ​​a preset size containing the bone edge from the low-energy image, takes it as the region of interest (ROI), that is, the area to be tested, calculates the relative standard deviation of the ROI area in the subtraction image under different pixels, takes the subtraction parameter corresponding to the minimum relative standard deviation as the soft tissue subtraction parameter, and then determines the bone subtraction parameter based on the empirical relationship between the bone and soft tissue subtraction parameters; the determination of the ROI of the present application does not involve complex feature extraction, and the image ROI actually used for calculation is much smaller than the image size, which greatly improves the efficiency of the algorithm. In principle, the algorithm has good robustness and low computational overhead, is easy to implement, and provides a guarantee for clinical rapid acquisition of accurate dual-energy subtraction results, and lays a foundation for further imaging diagnosis; in addition, the relative standard deviation curve usually has a good convex property, which ensures that the search algorithm can obtain a relatively stable optimal value, and the relative standard deviation is based on the bone edge visibility as the judgment criterion. The judgment result is more in line with the intuitive result of the human eye, the effect is relatively good, and it can basically meet the clinical requirements at one time without manual fine-tuning.

[0130] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0131] Computer-readable signal media may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0132] The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed entirely on the operator's computer, partially on the operator's computer, as an independent software package, partially on the operator's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the operator's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet). The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts between the various embodiments can be referred to each other.

[0133] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0134] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0135] The above is a detailed introduction to the calculation method, device, equipment and storage medium of the dual-energy subtraction parameters provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for calculating dual-energy subtraction parameters, which is used to calculate soft tissue subtraction parameters and bone subtraction parameters in shadow areas of dual-energy X-ray medical images, wherein: The dual-energy X-ray medical image includes a high-energy image and a low-energy image, and is characterized in that it includes the steps of: Acquire a target dual-energy X-ray medical image, and perform registration processing on the target high-energy image and the target low-energy image; Determine the area to be tested according to the position of the maximum value of the gradient amplitude image of the preset area in the target low-energy image after the registration processing, and determine the high-energy sub-image block corresponding to the area to be tested in the target high-energy image and the low-energy sub-image block corresponding to the area to be tested in the target low-energy image; Determine a subtraction image corresponding to each pixel in the area to be tested based on the high-energy sub-image block and the low-energy sub-image block, determine a standard deviation and a mean of the subtraction image, and generate a relative standard deviation corresponding to each pixel in the area to be tested based on the standard deviation and the mean of the subtraction image; The soft tissue subtraction parameter and the bone subtraction parameter are determined according to the minimum value of the relative standard deviation.

2. The method for calculating dual-energy subtraction parameters according to claim 1, characterized in that: The step of determining the area to be tested according to the position of the maximum value of the gradient amplitude image of the preset area in the target low-energy image after the registration processing, and determining the high-energy sub-image block corresponding to the area to be tested in the target high-energy image and the low-energy sub-image block corresponding to the area to be tested in the target low-energy image, comprises: Determine the position of a preset area in the target low-energy image after the registration process, and generate the gradient amplitude image based on pixels in the preset area; Obtaining the position of the maximum value of the gradient amplitude image, and converting the position of the maximum value of the gradient amplitude image according to the position of the preset area in the target low-energy image to determine the target position, and setting the neighborhood position of the target position as the area to be measured; According to the position of the area to be tested, a high-energy sub-image block corresponding to the area to be tested in the target high-energy image and a low-energy sub-image block corresponding to the area to be tested in the target low-energy image are determined.

3. The method for calculating dual-energy subtraction parameters according to claim 2, characterized in that: The step of determining the position of a preset area in the target low-energy image after the registration process, and generating the gradient amplitude image according to pixels in the preset area, comprises: Determining the position of a preset area in the target low-energy image after the registration process; Performing low-pass filtering on pixels in a preset area of ​​the target low-energy image; The gradient magnitude image is generated according to the pixels in the preset area after low-pass filtering.

4. The method for calculating dual-energy subtraction parameters according to claim 3, characterized in that: The step of generating the gradient amplitude image based on pixels in a preset area after low-pass filtering comprises: Determine the x component and y component of the pixel gradient in the preset area after low-pass filtering; The gradient magnitude image is generated according to the x component and the y component of the pixel gradient in the preset area.

5. The method for calculating dual-energy subtraction parameters according to claim 1, characterized in that: The step of determining the subtraction image corresponding to each pixel in the area to be tested based on the high-energy sub-image block and the low-energy sub-image block, determining the standard deviation and mean of the subtraction image, and generating the relative standard deviation corresponding to each pixel in the area to be tested based on the standard deviation and mean of the subtraction image, comprises: Determine the subtraction image corresponding to each pixel in the test area according to the difference in energy attenuation mode and photoelectric absorption effect of bone and soft tissue on X-ray photons and the ratio of the high-energy sub-image block to the low-energy sub-image block corresponding to each pixel in the test area; Determine the standard deviation and mean of the subtraction image corresponding to each pixel in the area to be tested; The relative standard deviation corresponding to each pixel in the area to be measured is generated according to the ratio of the standard deviation to the mean of the subtraction image corresponding to each pixel in the area to be measured.

6. The method for calculating dual-energy subtraction parameters according to claim 1, characterized in that: The step of determining the soft tissue subtraction parameter according to the minimum value of the relative standard deviation, and determining the bone subtraction parameter according to the soft tissue subtraction parameter comprises: Determine the subtraction parameter corresponding to the minimum value of the relative standard deviation as the soft tissue subtraction parameter; The bone subtraction parameters are determined according to the soft tissue subtraction parameters and the preset weights.

7. The method for calculating dual-energy subtraction parameters according to claim 6, characterized in that: The step of determining the bone subtraction parameters according to the soft tissue subtraction parameters and the preset weights comprises: The bone subtraction parameter is determined according to the sum of the soft tissue subtraction parameter and the preset weight; wherein the value of the preset weight is 0.

25.

8. A dual-energy subtraction parameter calculation device, used to calculate the soft tissue subtraction parameters and bone subtraction parameters of the shadow area in the dual-energy X-ray medical image, wherein: The dual-energy X-ray medical image includes a high-energy image and a low-energy image, and is characterized in that it includes: A registration module is used to acquire a target dual-energy X-ray medical image and perform registration processing on a target high-energy image and a target low-energy image; A determination module, used to determine the area to be tested according to the position of the maximum value of the gradient amplitude image of the preset area in the target low-energy image after the registration process, and determine the high-energy sub-image block corresponding to the area to be tested in the target high-energy image and the low-energy sub-image block corresponding to the area to be tested in the target low-energy image; A calculation module, used to determine the subtraction image corresponding to each pixel in the area to be measured based on the high-energy sub-image block and the low-energy sub-image block, and determine the standard deviation and mean of the subtraction image, and generate the relative standard deviation corresponding to each pixel in the area to be measured based on the standard deviation and mean of the subtraction image; An output module is used to determine the soft tissue subtraction parameter and the bone subtraction parameter according to the minimum value of the relative standard deviation.

9. A computer device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program implements the method according to any one of claims 1 to 7 when executed by the processor.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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