Medical image segmentation method and system for chronic obstructive pulmonary disease

Abstract

The invention provides a medical image segmentation method and system for a chronic obstructive pulmonary disease, and belongs to the field of medical image processing, and the method comprises the steps: obtaining a chronic obstructive pulmonary disease image, converting the chronic obstructive pulmonary disease image into a gray image, carrying out the non-local mean filtering operation of the gray image, and obtaining a filtered image; constructing a corresponding two-dimensional histogram based on the filtered image and the gray level image; a self-adaptive transmission strategy and a spiral motion operator are introduced into an artemisinin algorithm, and an improved artemisinin algorithm is obtained; taking the Renyi entropy as a target, taking an improved artemisinin algorithm as a search method, and carrying out threshold search to obtain an optimal segmentation threshold combination; and segmenting the chronic obstructive pulmonary disease image according to the optimal segmentation threshold combination, and outputting a segmentation result. And accurate technical support is provided for early diagnosis of the chronic obstructive pulmonary disease.

Description

Technical Field

[0001] The present invention belongs to the field of medical image processing, and in particular relates to a medical image segmentation method and system for chronic obstructive pulmonary disease. Background Art

[0002] Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease. Early and accurate diagnosis and evaluation of COPD are of great significance for the treatment and management of patients. However, due to the fuzzy tissue boundaries and complex lesion morphology of the lesion area in lung CT images, traditional imaging diagnostic methods are prone to misdiagnosis and low efficiency. Therefore, how to efficiently and accurately segment the lesion area in medical images has become an important research direction for COPD diagnosis.

[0003] As an effective medical image processing technology, multi-threshold image segmentation can divide the image into different areas by setting multiple thresholds, so as to accurately extract the target lesions. Its principle is based on the pixel grayscale histogram of the image, and by selecting an appropriate threshold combination to maximize a certain criterion (such as entropy) to achieve the best image segmentation effect. However, the main difficulty faced in multi-threshold segmentation is how to choose a suitable threshold combination. Especially in the high-dimensional and complex threshold search space, traditional methods often have problems such as insufficient segmentation accuracy or long time consumption, which is difficult to meet actual needs. Meta-heuristic algorithms are often used for multi-threshold segmentation of medical images due to their powerful performance in global optimization problems. Of course, meta-heuristic algorithms also have problems such as easy to fall into local optimality and slow convergence speed, resulting in low accuracy of threshold combination selection when performing threshold segmentation on medical images, resulting in inaccurate medical image segmentation, which limits its performance in practical applications. Summary of the invention

[0004] In order to solve the multi-threshold segmentation problem of chronic obstructive pulmonary disease images, the present invention provides a method and system for segmenting medical images of chronic obstructive pulmonary disease.

[0005] In order to achieve the above object, the present invention provides the following technical solutions: A medical image segmentation method for chronic obstructive pulmonary disease specifically comprises the following steps: Acquire a medical image of chronic obstructive pulmonary disease, convert the medical image of chronic obstructive pulmonary disease into a grayscale image, perform a non-local mean filtering operation on the grayscale image to obtain a filtered image; obtain a two-dimensional histogram based on the filtered image and the grayscale image; The adaptive transfer strategy and the spiral motion operator are introduced into the artemisinin algorithm to obtain an improved artemisinin algorithm; based on the two-dimensional histogram, the Renyi entropy with the maximum total entropy is obtained as a target, and the improved artemisinin algorithm is used as a search method to obtain the best segmentation threshold combination; The chronic obstructive pulmonary disease medical image is segmented according to the optimal segmentation threshold combination, and the segmentation result is output.

[0006] Preferably, the improved artemisinin algorithm updates individual positions through a spiral path, and the position update rule is specifically as follows: ; ; ; ; ; in, For the The individual in The location of the dimension, is the sorted population; is the distance between the population and the sorted population; is a constant equal to 1, used to control the shape of the logarithmic spiral function; Represents the angle change part of the spiral path, which makes the individual move along the spiral shape in different dimensions; Indicates rounding; is the current evaluation number; and is the process parameter; is the maximum number of evaluations; Indicates the population size; After the position of the individual in the population is updated, the migration process of the individual is dynamically adjusted through adaptive perturbations. The specific update rules for individual migration are: ; ; ; in, is a disturbance amplitude control parameter; is a random number in the range of [-1,1], used to introduce randomness into the search process; Used to adjust the position of the current solution; population individuals Need to pass To update, , () represents a random function, p To randomly arrange the dimensions.

[0007] Preferably, a non-local mean filtering operation is performed on the grayscale image to obtain a filtered image, specifically: ; in, is the pixel in the medical image of chronic obstructive pulmonary disease Gray value after denoising by non-local mean filtering; Refers to pixels and The corresponding weight of Chronic obstructive pulmonary disease medical image I Medium Pixels f The gray value of .

[0008] Preferably, based on the filtered image and the grayscale image, a two-dimensional histogram is obtained, specifically, the point It is a grayscale image Pixels in Gray value and non-local mean image Pixels in The gray value is generated by Indicate point The number of occurrences is given by the following formula based on the joint probability density, resulting in a two-dimensional histogram: .

[0009] Preferably, the Renyi entropy with the maximum total entropy is obtained based on the two-dimensional histogram as a target, and the improved artemisinin algorithm is used as a search method to obtain the best segmentation threshold combination, specifically: Let the grayscale level of the two-dimensional histogram be , Represents a non-zero natural number, pixel grayscale level and non-local mean grayscale Constitute a The binary group , Renyi entropy The expression is: ; ; ; … ; ; ; … ; in Represents different entropy values ​​for image classification, Representing different categories of possibilities; By pairing two Set the Renyi total entropy to the maximum and get the maximum Renyi entropy ; The improved artemisinin algorithm was used as the search method to obtain the best segmentation threshold combination with the Renyi entropy with the maximum total entropy as the goal.

[0010] The present invention also provides a medical image segmentation system for chronic obstructive pulmonary disease, specifically comprising: An image processing module, used for acquiring a medical image of chronic obstructive pulmonary disease, converting the medical image of chronic obstructive pulmonary disease into a grayscale image, performing a non-local mean filtering operation on the grayscale image to obtain a filtered image; and obtaining a two-dimensional histogram based on the filtered image and the grayscale image; A threshold acquisition module is used to introduce the adaptive transfer strategy and the spiral motion operator into the artemisinin algorithm to obtain an improved artemisinin algorithm; based on the two-dimensional histogram, the Renyi entropy with the maximum total entropy is obtained as a target, and the improved artemisinin algorithm is used as a search method to obtain the best segmentation threshold combination; The image segmentation module is used to segment the chronic obstructive pulmonary disease medical image according to the optimal segmentation threshold combination and output the segmentation result.

[0011] The present invention also provides a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps described in the medical image segmentation method for chronic obstructive pulmonary disease.

[0012] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is loaded by a processor, the steps described in the medical image segmentation method for chronic obstructive pulmonary disease can be executed.

[0013] The medical image segmentation method for chronic obstructive pulmonary disease provided by the present invention has the following beneficial effects: The present invention converts the acquired chronic obstructive pulmonary disease image into a grayscale image and performs non-local mean filtering, and then forms a two-dimensional histogram with the non-local mean filtered image and the grayscale image. The adaptive transfer strategy and the spiral motion operator are introduced into the artemisinin algorithm, which improves the diversity and global search performance of the algorithm, adjusts the individual migration process through dynamic perturbations, and flexibly switches between global exploration and local development at different stages. Selecting Renyi entropy as the target and the improved artemisinin algorithm as the search method, the best segmentation threshold combination of the global optimal solution is obtained, the accuracy of the threshold combination selection is improved, and the chronic obstructive pulmonary disease image is segmented according to the obtained optimal threshold combination, so as to achieve fast and accurate segmentation of the lesion area. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiment of the present invention and its design scheme, the following briefly introduces the drawings required for this embodiment. The drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 The present invention is a flowchart of a medical image segmentation method for chronic obstructive pulmonary disease.

[0016] Figure 2 It is a plan view of a two-dimensional histogram in a medical image segmentation method for chronic obstructive pulmonary disease in an embodiment of the present invention.

[0017] Figure 3 The figure is a processing flow chart of the improved artemisinin algorithm (ADSPAO) in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the technical solution of the present invention and implement it, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0019] Example The present invention provides a medical image segmentation method for chronic obstructive pulmonary disease, such as Figure 1 As shown, the specific steps include: S1: Obtain a COPD medical image and initialize the threshold segmentation level.

[0020] S2: Convert the COPD medical image into a grayscale image and perform a non-local mean filtering operation based on the grayscale image.

[0021] The non-local mean filtering method is an effective method to remove noise. Specifically, assuming and is an image I Medium Pixels e and f Gray value, image I The non-local mean of is calculated by the following formula: ; ; ; ; ; in, is the pixel in the image Gray value after non-local mean denoising, Refers to pixels and The corresponding weight of represents the normalization coefficient, Refers to the control parameters, and is the local mean, Refers to pixels The surrounding area is The pixel block, Refers to pixels The surrounding area is If the image Grayscale image of The size is , then the non-local mean image obtained by non-local mean filtering is The size is also , and is the pixel in the image , The grayscale values ​​of pixels e and f The pixel grayscale value in the local neighborhood of Located in pixels e One around Size of area Inside, It is in pixels e A pixel in the neighborhood of Same reason.

[0022] S3: Based on the non-local mean filter image and the grayscale image, construct the corresponding two-dimensional histogram. Figure 2As shown in , the corresponding non-local mean two-dimensional histogram can be generated through the non-local mean image and the grayscale image. It is a grayscale image Pixels in Gray value and non-local mean image Pixels in The gray value is generated by Indicate point The number of occurrences and their joint probability density are given by the following formula, and the final two-dimensional histogram is obtained.

[0023] .

[0024] S4: Let the gray levels of the two-dimensional histogram be , pixel gray level and non-local mean grayscale Constitute a The binary group , then Renyi entropy The expression is: ; ; ; … ; ; ; … ; in Represents different entropy values ​​for image classification, Represents the possibilities of different categories.

[0025] For two-tuple Set the Renyi total entropy to the maximum, and get .

[0026] S5: The adaptive transfer strategy and spiral motion operator were introduced into the artemisinin algorithm to obtain the improved artemisinin algorithm.

[0027] S6: Using the improved artemisinin algorithm as the search method, the processing flow is as follows Figure 3 As shown in Figure 1, Renyi entropy is used as the target and the optimal threshold combination is obtained based on the two-dimensional histogram.

[0028] S6.1: Initialization parameters, including population size , the maximum number of evaluations , dimension wait.

[0029] S6.2: Initialize the population as shown below. Calculate the fitness value of all individuals, i.e. Renyi entropy, and find the optimal individual and the optimal fitness value ; ; in, , is the population size; , is the dimension size; and Respectively represent Maximum and minimum bounds in each dimension; Used to generate a random number between 0 and 1.

[0030] S6.3: Entering the comprehensive elimination stage, simulating the process in which high-dose drugs spread rapidly and act extensively on various parts of the human body during the early treatment of malaria. The mathematical model of this stage is as follows: ; in, Indicates The individual in generation The location of the dimension; Represents the current best position; As a probability coefficient, it will be dynamically adjusted as the optimization process progresses. In the early stages of optimization, it is better to set a larger The value strengthens the global search and gradually decreases in the later stages. Turning to local fine optimization, It is a decay index related to drug concentration, simulating the exponential decay process of drug concentration over time. The decay model of drug concentration is as follows: ; ; in, Indicates time The drug concentration at is the initial drug concentration; is the decay rate constant.

[0031] S6.4: Enter the local clearance phase, simulating the process in which low-dose drugs continue to act on the remaining pathogens in the late stage of treatment. The update process of this phase is as follows: ; ; in, are three search agents selected at random; is a random coefficient between [0.1,0.6]; It is The normalized fitness value of a search agent indicates the probability of the search agent being selected, and the quality of the solution is further optimized through local fine search; Indicates The fitness value of the search agent Represent the minimum and maximum fitness values ​​respectively.

[0032] S6.5: Enter the post-consolidation phase to simulate the risk of disease recurrence that may occur after treatment. The update rules for this process are as follows: .

[0033] S6.6: Calculate the fitness values ​​of the updated individuals in the population and sort the individuals in the population according to the fitness values. Introduce the spiral migration operator to update the individual positions through a spiral path to achieve the purpose of balancing exploration and development. The update rules are as follows: ; ; ; ; ; in, is the sorted population; is the distance between the population and the sorted population; is a constant equal to 1, used to control the shape of the logarithmic spiral function; Represents the angle change part of the spiral path, which makes the individual move along the spiral shape in different dimensions; Indicates rounding; is the current evaluation number; and is the process parameter.

[0034] S6.7: Execute the adaptive transfer strategy to dynamically adjust the migration process of individuals by introducing adaptive perturbations. In order to improve the global search capability of the artemisinin algorithm, a dynamic perturbation operator is added so that the perturbation intensity can gradually decay with the iteration process, thereby achieving a gradual transition between exploration and development. Specifically, the update of individuals can be described by the following formula: ; ; ; in, is a disturbance amplitude control parameter; is a random number in the range of [-1,1], used to introduce randomness into the search process; Used to adjust the position of the current solution in some cases; population individuals Need to pass To update, ,and p Randomize the dimensions. This random dimension selection increases the diversity of the algorithm, causing individuals to update different dimensions in different iterations, thereby enhancing the search ability.

[0035] S6.8: Use the greedy selection method to compare the newly generated solution with the initial solution. If the newly generated solution is better than the original solution, it will be updated. At the same time, the updated solution will be compared with the optimal solution. If it is better than the optimal solution, the optimal individual solution will be updated. and the optimal fitness value bestfitness That is, assume The fitness value of was originally 10, and the new value is The fitness value is 100, 100 is greater than 10, use Replacement, that is = .

[0036] S6.9: Check whether , if satisfied, continue to execute S6.3: otherwise, output the optimal position and optimal fitness value.

[0037] The optimal position and the optimal fitness value are output as the optimal threshold combination and the maximum Renyi entropy.

[0038] S7: Segment the COPD medical image according to the optimal threshold combination and output the segmentation result.

[0039] The following table shows the three image quality evaluation indicators using the Wilcoxon signed rank test, namely peak signal-to-noise ratio Peak Signal to Noise Ratio, PSNR), structural similarity index StructuralSimilarity, SSIM) and Feature Similarity (FSIM) are used to evaluate the quality of the segmented image. These three evaluation indicators are used to evaluate the difference between the segmented image and the original image, measure the structural integrity of the image, and reflect the feature similarity between the original image and the segmented image. In the table, Thresholds represents the threshold level, Mean represents the average of the overall ranking, Rank represents the ranking level, "+" indicates that the performance of ADSPAO is better than the comparison algorithm, "=" indicates that the performance of ADSPAO is the same as the comparison algorithm, and "-" indicates that the performance of ADSPAO is worse than the comparison algorithm. According to the results of the Wilcoxon signed rank test, it can be seen that among all threshold levels, the image segmented by the threshold set obtained using ADSPAO shows superiority in multiple angles, with an overall average ranking of the first, and has certain stability and robustness, which can effectively maintain the details and visual quality of the segmented image. CGPSO (Particle Swarm Optimization combining chaos and Gaussian local search procedure) is a particle swarm optimization that combines chaos and Gaussian local search procedure; MGSMA (Enhanced Slime Mould Algorithm based on a new movement mechanismand Gaussian kernel probability strategy) is an enhanced slime mold algorithm based on a new movement strategy and Gaussian kernel probability strategy; MDE (Modified Differential Evolution) is an improved differential evolution algorithm; IGWO (Improved Grey Wolf Optimizer) is an improved grey wolf optimization algorithm; GLSMA (Improved Slime Mould Algorithm based on Gaussian mutation and Lévy flights) is an improved slime mold algorithm based on Gaussian mutation and Lévy flights; WOA (Whale Optimization Algorithm) is a whale optimization algorithm.

[0040] Table 1. Comparison results of PSNR

[0041] Table 2 Comparison results of SSIM

[0042] Table 3 Comparison results of FSIM

[0043] The present invention also provides a medical image segmentation system for chronic obstructive pulmonary disease, specifically comprising: The image processing module is used to obtain a medical image of chronic obstructive pulmonary disease, convert the medical image of chronic obstructive pulmonary disease into a grayscale image, perform a non-local mean filtering operation on the grayscale image to obtain a filtered image; and obtain a two-dimensional histogram based on the filtered image and the grayscale image.

[0044] The threshold acquisition module is used to introduce the adaptive transfer strategy and the spiral motion operator into the artemisinin algorithm to obtain the improved artemisinin algorithm; based on the two-dimensional histogram, the Renyi entropy with the maximum total entropy is obtained as the goal, and the improved artemisinin algorithm is used as the search method to obtain the best segmentation threshold combination.

[0045] The image segmentation module is used to segment the medical image of chronic obstructive pulmonary disease according to the optimal segmentation threshold combination and output the segmentation result.

[0046] Each module in the above-mentioned medical image segmentation system for chronic obstructive pulmonary disease can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.

[0047] The present invention also provides a computer device, including a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps in an embodiment of a medical image segmentation method for chronic obstructive pulmonary disease. The specific implementation method can be found in the method embodiment, which will not be repeated here.

[0048] Furthermore, the present invention also provides a non-temporary computer-readable storage medium containing instructions, and a computer program is stored on the storage medium. For example, a memory containing instructions, the above instructions can be executed by a processor of a computer device to complete the above method. For example, the non-temporary computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a tape, a floppy disk, and an optical data storage device. When the computer program is executed by the processor, the steps in an embodiment of a medical image segmentation method for chronic obstructive pulmonary disease can be implemented. The specific implementation method can be referred to the method embodiment, which will not be repeated here.

[0049] It should be understood by those skilled in the art that embodiments of the present invention may provide methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0050] The present invention is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as a combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0051] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0052] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0053] It should be pointed out that the specific implementation methods described above can enable those skilled in the art to understand the invention more comprehensively, but do not limit the invention in any way. Therefore, although the invention has been described in detail in this specification and embodiments, those skilled in the art should understand that the invention can still be modified or replaced by equivalents; and all technical solutions and improvements that do not deviate from the spirit and scope of the invention are included in the protection scope of the patent for the invention. Any figure mark in the claims should not be regarded as limiting the claims involved. Any simple change or equivalent replacement of the technical solution that can be obviously obtained by any technician familiar with the field within the technical scope disclosed in the present invention belongs to the protection scope of the present invention.

Claims

1. A medical image segmentation method for chronic obstructive pulmonary disease, characterized in that: The following steps are involved: Acquire a medical image of chronic obstructive pulmonary disease, convert the medical image of chronic obstructive pulmonary disease into a grayscale image, and perform a non-local mean filtering operation on the grayscale image to obtain a filtered image; Obtaining a two-dimensional histogram based on the filtered image and the grayscale image; The adaptive transfer strategy and the spiral motion operator are introduced into the artemisinin algorithm to obtain an improved artemisinin algorithm; based on the two-dimensional histogram, the Renyi entropy with the maximum total entropy is obtained as a target, and the improved artemisinin algorithm is used as a search method to obtain the best segmentation threshold combination; The chronic obstructive pulmonary disease medical image is segmented according to the optimal segmentation threshold combination, and the segmentation result is output.

2. The medical image segmentation method for chronic obstructive pulmonary disease according to claim 1, characterized in that: The improved artemisinin algorithm updates individual positions through a spiral path, and the position update rule is specifically as follows: ； ； ； ； ； in, For the The individual in The location of the dimension, is the sorted population; is the distance between the population and the sorted population; is a constant equal to 1, used to control the shape of the logarithmic spiral function; Represents the angle change part of the spiral path, which makes the individual move along the spiral shape in different dimensions; Indicates rounding; is the current evaluation number; and is the process parameter; is the maximum number of evaluations; Indicates the population size; After the position of the individual in the population is updated, the migration process of the individual is dynamically adjusted through adaptive perturbations. The update rules of individual migration are as follows: ； ； ； in, is a disturbance amplitude control parameter; is a random number in the range of [-1,1], used to introduce randomness into the search process; Used to adjust the position of the current solution; population individuals Need to pass To update, , () represents a random function, p To randomly arrange the dimensions.

3. The medical image segmentation method for chronic obstructive pulmonary disease according to claim 1, characterized in that: Perform a non-local mean filtering operation on the grayscale image to obtain a filtered image, specifically: ； in, is the pixel in the medical image of chronic obstructive pulmonary disease Gray value after denoising by non-local mean filtering; Refers to pixels and The corresponding weight of Chronic obstructive pulmonary disease medical image I Medium Pixels f The gray value of .

4. The medical image segmentation method for chronic obstructive pulmonary disease according to claim 3, characterized in that: Based on the filtered image and the grayscale image, a two-dimensional histogram is obtained, specifically, the point It is a grayscale image Pixels in Gray value and non-local mean image Pixels in The gray value is generated by Indicate point The number of occurrences is given by the following formula based on the joint probability density, resulting in a two-dimensional histogram: 。 5. The medical image segmentation method for chronic obstructive pulmonary disease according to claim 2, characterized in that: Based on the two-dimensional histogram, the Renyi entropy with the maximum total entropy is taken as the goal, and the improved artemisinin algorithm is used as the search method to obtain the optimal segmentation threshold combination, which is specifically: Let the grayscale level of the two-dimensional histogram be , Represents a non-zero natural number, pixel grayscale level and non-local mean grayscale Constitute a The binary group , Renyi entropy The expression is: ； ； ； … ； ； ； … ； in, Represents different entropy values ​​for image classification, Representing different categories of possibilities; By pairing two Set the Renyi total entropy to the maximum and get the maximum Renyi entropy ; The improved artemisinin algorithm was used as the search method to obtain the optimal segmentation threshold combination with the Renyi entropy with the maximum total entropy as the goal.

6. A medical image segmentation system for chronic obstructive pulmonary disease, characterized in that: include: An image processing module, used for acquiring a medical image of chronic obstructive pulmonary disease, converting the medical image of chronic obstructive pulmonary disease into a grayscale image, and performing a non-local mean filtering operation on the grayscale image to obtain a filtered image; Obtaining a two-dimensional histogram based on the filtered image and the grayscale image; A threshold acquisition module is used to introduce the adaptive transfer strategy and the spiral motion operator into the artemisinin algorithm to obtain an improved artemisinin algorithm; based on the two-dimensional histogram, the Renyi entropy with the maximum total entropy is obtained as a target, and the improved artemisinin algorithm is used as a search method to obtain the best segmentation threshold combination; The image segmentation module is used to segment the chronic obstructive pulmonary disease medical image according to the optimal segmentation threshold combination and output the segmentation result.

7. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is loaded into a processor, it can execute the steps of the method according to any one of claims 1 to 5.

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