Medical image denoising method based on 3D chaotic mapping dung beetle algorithm

Abstract

The invention relates to the technical field of image processing, and discloses a medical image denoising method based on a 3D chaotic mapping dung beetle algorithm, and the method comprises the following steps: 1, inputting a to-be-processed original medical image, carrying out the noise adding of the original medical image, and generating a noisy image; step 2, inputting the noisy image into a hybrid filter used for denoising the image; 3, calculating an optimal parameter combination of each filter in the hybrid filter by adopting a dung beetle optimization algorithm, and configuring the hybrid filter by adopting the optimal parameter combination; step 4, performing denoising processing on the noisy image by using the configured hybrid filter to generate a denoised image; wherein in the step 3, the distribution position of the population is initialized by adopting 3D chaotic mapping; the technical problem of poor medical image denoising effect in the prior art is solved.

Description

Technical Field

[0001] The invention relates to the technical field of image processing, and in particular to a medical image denoising method based on a 3D chaotic mapping dung beetle algorithm. Background Art

[0002] Medical images, such as X-rays, CT scans, and MRI images, are important bases for doctors to diagnose diseases. However, due to the limitations of imaging equipment and individual differences among patients, medical images often contain varying degrees of noise. This noise may mask important pathological information, leading to misdiagnosis or missed diagnosis. Therefore, improving the clarity and signal-to-noise ratio of medical images through image denoising technology is of great significance for improving the accuracy and reliability of diagnosis.

[0003] Many existing filtering techniques reduce noise in images by smoothing or blurring. However, these techniques often lead to the loss of complex details in the image, especially in edge and texture areas. Therefore, finding a balance between noise reduction and retaining important image features is a challenging task. The parameter configuration of the filter is the key to achieving this balance. The choice of parameters directly affects the filtering effect, including the degree of noise removal and the retention of image details. In order to automatically calculate the optimal parameters of the filter, existing technologies use meta-heuristic algorithms, such as genetic algorithms, particle swarm optimization algorithms, etc. These algorithms search for the optimal solution by simulating natural processes (such as genetic evolution, group behavior, etc.).

[0004] In metaheuristic algorithms, the distribution position of the initial population has a significant impact on the accuracy and convergence speed of the algorithm. A reasonable initial population distribution can accelerate the convergence process of the algorithm and improve the search efficiency; while an unreasonable initial population distribution may cause the algorithm to fall into a local optimal solution and reduce the search accuracy. In order to solve the problem of uncertain distribution of the initial population, the initial position of the population should be evenly distributed in the entire search space to ensure its diversity and traversal. However, generating only random position vectors cannot guarantee efficient traversal and diversity.

[0005] A commonly used method in the prior art is chaos mapping. Chaos mapping is a nonlinear dynamic system with pseudo-randomness and ergodicity, which can generate time series with complex behaviors. Commonly used chaos mappings include tent chaos mapping, logical chaos mapping, and sine chaos mapping. Although the use of chaos mapping makes it possible for the algorithm to escape the local optimal solution, it still has the defect of serious randomness. That is, although the initial population position is diverse, it lacks certain regularity. This causes the algorithm to produce large fluctuations and uncertainties during the search process, affecting the search efficiency and accuracy. Summary of the invention

[0006] The present invention aims to provide a medical image denoising method based on a 3D chaotic mapping dung beetle algorithm to solve the technical problem of poor medical image denoising effect in the prior art.

[0007] To achieve the above object, the present invention adopts the following technical solution: a medical image denoising method based on 3D chaotic mapping dung beetle algorithm, comprising the following steps:

[0008] Step 1, inputting an original medical image to be processed, performing noise processing on the original medical image, and generating a noisy image;

[0009] Step 2, inputting the noisy image into a hybrid filter for denoising the image;

[0010] Step 3, using the dung beetle optimization algorithm to calculate the optimal parameter combination of each filter in the hybrid filter, and using the optimal parameter combination to configure the hybrid filter;

[0011] Step 4, using the configured hybrid filter to denoise the noisy image to generate a denoised image;

[0012] Among them, in step 3, the distribution position of the population is initialized using 3D chaotic mapping, and the 3D chaotic mapping is:

[0013]

[0014] Among them, x, y, z are system state variables, a=-0.6, c=-10, 1≤b≤7, and the initial values ​​are set to (0.1, 0, 0).

[0015] The principle and advantages of this scheme are: the original medical image to be processed is input and subjected to noise processing to generate a noisy image. By artificially adding a certain amount of noise (such as Gaussian noise, salt and pepper noise, etc.) to the image, the noise component in the image can be made more obvious and easier to identify. The noisy image is input into a hybrid filter. The hybrid filter is composed of multiple filters, and is intended to improve the denoising effect by combining the advantages of different filters.

[0016] The dung beetle optimization algorithm is used to calculate the optimal parameter combination of each filter in the hybrid filter. The 3D chaotic mapping is used to initialize the distribution position of the dung beetle population. The 3D chaotic mapping has pseudo-randomness and ergodicity, and can generate time series with complex behaviors, thereby ensuring the diversity and ergodicity of the initial population. Through the 3D chaotic mapping, the initial position of the population is evenly distributed in the entire search space, reducing the possibility of falling into the local optimal solution.

[0017] This paper proposes a new type of 3D chaotic mapping, which is highly sensitive to initial conditions and can generate random sequences with unique characteristics. Even slight differences in initial conditions can lead to completely different evolutionary trajectories, thus ensuring a uniform distribution of the population and reducing the possibility of falling into local optimality.

[0018] The dung beetle optimization algorithm combined with 3D chaotic mapping shows more flexible characteristics in the global search range. By iteratively searching for the optimal filter parameter combination, this method can adapt to the complex characteristics of medical images and effectively deal with various medical noises. This method effectively retains important image features such as edge and texture information during the denoising process, thereby improving the visual quality and diagnostic value of medical images.

[0019] The 3D chaotic mapping algorithm ensures the uniformity of the population when initializing the dung beetle population, effectively reducing the convergence time, alleviating the computational burden, and speeding up the processing speed. By reducing defects such as noise and artifacts, this method improves the quality of medical images and helps reduce data redundancy, thereby simplifying the storage and transmission of medical images.

[0020] Preferably, as an improvement, in step 3, the optimal value of b is 3.7.

[0021] The beneficial effect of this improvement is that by using the Lyapunov index spectrum (Lyapunov index) and bifurcation diagram to carefully analyze the parameter b of the 3D chaotic mapping, it is found that when b = 3.7, the curvature of the Lyapunov index reaches its maximum value, which means that the system has the strongest chaotic characteristics and the best ergodicity under this parameter. Therefore, determining the optimal value of parameter b as 3.7 can significantly improve the efficiency and effect of 3D chaotic mapping when initializing the dung beetle population, thereby enhancing the global search capability and convergence speed of the dung beetle optimization algorithm.

[0022] Preferably, as an improvement, in step 2, the hybrid filter includes: a Gaussian filter, a median filter, a mean filter, a non-local mean filter and a sharpening filter.

[0023] The beneficial effect of this improvement is that the Gaussian filter, median filter, mean filter, non-local mean filter and sharpening filter are combined into a hybrid filter, which can make full use of their respective advantages to achieve a more comprehensive denoising effect. The Gaussian filter can effectively reduce Gaussian noise; the median filter is good at removing salt and pepper noise; although the mean filter will slightly reduce the image sharpness, it can simplify the image structure in some cases; the non-local mean filter can retain more details; and the sharpening filter can enhance the edges and details of the image. This combination can cope with many types of noise while retaining the important features of the image as much as possible.

[0024] Preferably, as an improvement, the dung beetle algorithm includes a rolling ball behavior, and in the rolling behavior, the calculation formula for updating the dung beetle position is:

[0025]

[0026] x i (t+1) is the position of the i-th dung beetle at the t-th iteration, k∈(0,0.2] is a constant value indicating the deflection coefficient, b∈(0,1) is a constant; α is -1 or 1, X w is the global worst position, Δx is the degree of change in light intensity;

[0027] When α=1, it indicates that the natural environment does not affect the original direction, and when α=-1, it indicates that the original direction is deviated from.

[0028] The beneficial effect of this improvement is that the deflection coefficient and the degree of light intensity change are introduced into the rolling behavior, so that the dung beetle can adjust the direction according to the current environment (i.e., light intensity) during the rolling process. When the environment is stable (α=1), the dung beetle keeps rolling in the original direction; when the environment changes (α=-1), the dung beetle deviates from the original direction. This mechanism increases the diversity of the dung beetle's search path, helps avoid falling into the local optimal solution, and improves the global search capability.

[0029] Preferably, as an improvement, the dung beetle algorithm includes dancing behavior, and during the dancing behavior, the calculation formula for updating the position of the dung beetle is:

[0030] x i (t+1)=x i (t)+tan(θ)|x i (t)-x i (t-1)|;

[0031] |x i (t)-x i (t-1)| is the difference between the position of the i-th dung beetle at the t-th iteration and the position at the t-1-th iteration; θ∈[0,π] is the deflection angle; when θ is equal to 0,π / 2,π, the dung beetle will not change its position.

[0032] The beneficial effect of this improvement is that during the dancing behavior, the dung beetle updates its position by calculating the position difference and the deflection angle. When the position difference and the deflection angle meet certain conditions, the dung beetle will not change its position, which helps to maintain stability during the search process. At the same time, this mechanism also allows the dung beetle to make small position adjustments when necessary to explore new search areas. Therefore, the dancing behavior can balance global search and local search, and improve the convergence speed and accuracy of the algorithm.

[0033] Preferably, as an improvement, the dung beetle algorithm includes a breeding behavior, in which the calculation formula for the egg-laying area is:

[0034] Lb * =max(X * ×(1-R), Lb), Ub * =min(X * ×(1+R),Ub);

[0035] Lb * , Ub * are the lower and upper limits of the spawning area, Lb and Ub are the lower and upper limits of the search space, X * is the optimal position in the current population, and the inertia weight R = 1-t / T max , T max It is the maximum number of iterations during algorithm iteration;

[0036] The calculation formula for the position of the incubation fecal ball is:

[0037] B i (t+1)=X * +b1×(B i (t)-Lb * )+b2×(B i (t)-Ub * );

[0038] B i (t) is the position of the i-th incubation ball at the t-th iteration, b1 and b2 represent two independent random vectors of size 1×D, and D is the dimension of the optimization problem.

[0039] The beneficial effect of this improvement is that during the reproduction behavior, the dung beetle population is updated and evolved by calculating the egg-laying area and the position of the dung balls for incubation. The egg-laying area is determined based on the optimal position in the current population and the upper and lower limits of the search space, ensuring that the newly generated dung balls for incubation are located within the search space. The calculation of the position of the dung balls for incubation combines randomness and the dimension of the optimization problem, increasing the diversity of the population. This reproduction mechanism helps to maintain the vitality and diversity of the population and improve the global search capability of the algorithm.

[0040] Preferably, as an improvement, the dung beetle algorithm includes foraging behavior, and in the foraging behavior, the calculation formula for the best foraging area is:

[0041] Lb b =max(X b ×(1-R), Lb), Ub b =min(X b ×(1+R),Ub);

[0042] X b is the global optimal position, Lb b , Ub bare the lower and upper limits of the optimal foraging area;

[0043] The calculation formula for the position update of the dung beetle is:

[0044] x i (t+1)=x i (t)+C1×(x i (t)-Lb b )+C2×(x i (t)-Ub b );

[0045] x i (t) represents the position information of the i-th dung beetle at the t-th iteration, C1 represents a random number following a normal distribution, and C2 represents a random vector belonging to (0,1).

[0046] The beneficial effect of this improvement is that during foraging behavior, by calculating the best foraging area and the position update formula of the dung beetle, the dung beetle is guided to approach the global optimal position. The best foraging area is determined based on the global optimal position and the upper and lower limits of the search space, providing a clear search direction for the dung beetle. The position update of the dung beetle combines the randomness of the normal distribution and the random vector, increasing the diversity of the search path. This foraging mechanism can accelerate the convergence process of the algorithm and improve the search efficiency.

[0047] Preferably, as an improvement, the dung beetle algorithm includes a stealing behavior, and during the stealing behavior, the position update calculation formula of the thief dung beetle is:

[0048] x i (t+1)=X b +S×g×(|x i (t)-X * |+|x i (t)-X b |);

[0049] where x i (t) represents the position of the i-th thief in the t-th iteration, g is a random vector of size 1×D, which follows a normal distribution, S represents a constant, and X b To compete for the best position for food, X * is the optimal position in the current population.

[0050] The beneficial effect of this improvement is that during the stealing behavior, the thief dung beetle imitates the behavior of the optimal position in the current population by calculating the position update formula. This mechanism introduces elements of competition and cooperation, allowing the dung beetle population to learn from each other and make progress together during the search process. At the same time, because the position update of the thief dung beetle combines the characteristics of randomness and normal distribution, it can improve the algorithm's search ability while maintaining population diversity. The stealing behavior helps the algorithm jump out of the local optimal solution and improve the global search ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 4 is a denoising flowchart of an embodiment of the present invention.

[0052] Figure 2 1 is the Lyapunov exponent spectrum and bifurcation diagram when the parameter b changes according to the embodiment of the present invention.

[0053] Figure 3 It is a phase diagram of the chaotic mapping according to an embodiment of the present invention.

[0054] Figure 4 This is a speckle denoising comparison effect diagram of an embodiment of the present invention.

[0055] Figure 5 This is a comparison diagram of Poisson denoising effects according to an embodiment of the present invention.

[0056] Figure 6 This is a comparison diagram of the salt and pepper denoising effect of an embodiment of the present invention.

[0057] Figure 7 This is a Gaussian denoising comparison effect diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0058] The following is further described in detail through specific implementation methods:

[0059] Example

[0060] Basically as attached Figure 1 As shown, the medical image denoising method based on 3D chaos mapping dung beetle algorithm includes the following steps:

[0061] Step 1, inputting an original medical image to be processed, performing noise processing on the original medical image, and generating a noisy image;

[0062] Step 2, inputting the noisy image into a hybrid filter for denoising the image;

[0063] Step 3, using the dung beetle optimization algorithm to calculate the optimal parameter combination of each filter in the hybrid filter, and using the optimal parameter combination to configure the hybrid filter;

[0064] Step 4, using the configured hybrid filter to denoise the noisy image to generate a denoised image;

[0065] Among them, in step 3, the distribution position of the population is initialized using 3D chaotic mapping, and the 3D chaotic mapping is:

[0066]

[0067] Among them, x, y, z are system state variables, a, b, c are system parameters; assume that system parameters a=-0.6, c=-10, 1≤b≤7, and the initial values ​​are set to (0.1, 0, 0).

[0068] The optimal value of parameter b is determined by using the Lyapunov index spectrum and bifurcation diagram. Then the initial condition of the system is set to (0.1, 0, 0), and the parameters a = -0.6 and c = -10 are set; the following is obtained: Figure 2 As shown, the upper figure is the Lyapunov index diagram, and the lower figure is the bifurcation diagram. When 1≤b≤7, the system is always in a chaotic state.

[0069] The curvature of the Lyapunov exponent reaches its maximum value when b = 3.7, so the optimal value of parameter b is determined to be 3.7. Figure 3 As shown in the figure, the chaotic mapping phase diagram is set as system parameters a = -0.6, b = 3.7, c = -10, and the system initial value is determined to be (0.1, 0, 0). Among them, (a) is 3d, (b) is the xy plane, (c) is the xz plane, and (d) is the yz plane.

[0070] The hybrid filter used in step 2 includes: a Gaussian filter (GF), a median filter (MDF), a mean filter (MF), a non-local mean filter (NLM) and a sharpening filter.

[0071] Gaussian filter (GF) smoothes the image by convolving it with a Gaussian function, thereby reducing noise and details. According to the set standard deviation o, a Gaussian distribution weight matrix (i.e., Gaussian kernel) is generated. The Gaussian kernel is convolved with the image, that is, for each pixel in the image, the pixel value in its neighborhood is multiplied by the weight corresponding to the Gaussian kernel and summed to obtain a smoothed new pixel value. Through this process, the Gaussian filter can effectively reduce high-frequency noise in the image while retaining the overall image structure. The expression formula of the Gaussian filter is as follows.

[0072]

[0073] In the formula, G(x, y) represents the distance between pixels; σ represents the standard deviation of Gaussian distribution. Gaussian filter applies Gaussian kernel to smooth the image. The smoothing effect is achieved by weighted average of the neighborhood pixel values ​​of each pixel. The weight is determined by Gaussian function, which can reduce the impact of noise on the image.

[0074] Median filter (MDF), the working principle of the median filter is to sort the neighborhood pixel values ​​of each pixel in the image, and then take the median as the new value of the pixel, so as to achieve the effect of removing noise, especially for salt and pepper noise. For a pixel I (x, y), its neighborhood is N (x, y) (for example, 3 × 3 window), the output of the median filter I filtered (x, y) is expressed as:

[0075] I filtered (x, y)median=I(i,j)|(i,j)∈N(x,y);

[0076] Where median represents the operation of finding the median; N(x, y) is the neighborhood of pixel I(x, y). The median filter removes salt and pepper noise or other noise by sorting the pixel values ​​of each pixel and its neighborhood in the image and replacing the original pixel value with the median value.

[0077] Mean filter (MF) is a filter that achieves image smoothing and noise reduction by averaging the pixel values ​​of each pixel in the image and its neighborhood. It can effectively reduce random noise in the image, but it may also cause blurring of image details. For a pixel I(x, y), its neighborhood is N(x, y) (for example, a 3×3 window), the output of the mean filter I filtered (x, y) is expressed as:

[0078]

[0079] Where |N(x,y)| is the number of pixels in the neighborhood. I(i,j) is the value of the pixel in the neighborhood N(x,y). The mean filter reduces the sharpness and clarity of the image by smoothing out details and noise in the image. This is because each pixel value becomes the average of all the pixel values ​​in its neighborhood, thereby reducing the differences between pixels.

[0080] Non-local mean filter (NLM), Non-local mean filter (NLM) performs smoothing and denoising by considering similar pixel blocks in the image, not just the local neighborhood. For each pixel, NLM not only uses the pixels in its local neighborhood, but also looks for small blocks similar to the pixel in the entire image (usually a window of fixed size), and the pixel values ​​of these similar blocks will be used to calculate the new pixel value. The similarity between similar pixel blocks is calculated using Gaussian weighting. The higher the similarity, the greater the weight. In the end, the filtered pixel value is the weighted average of the pixel values ​​in these similar blocks. The similarity is usually calculated based on the Euclidean distance (or other metrics). The smaller the distance, the more similar the pixel blocks, and the greater the weight. Given an image I, the value I after non-local mean filtering of a pixel I(p) in the image filtered (p) is represented by the following formula:

[0081]

[0082] Where p is a pixel position in the image; Ω is the area in the image where similar blocks are searched; I(q) is the pixel value at position q in the image; w(p, q) is the similarity weight between the pixel blocks at positions p and q. Non-local mean filtering can better preserve image detail information. By calculating similarity and performing weighted averaging, it can reduce the impact of noise and make the image clearer and more natural.

[0083] Sharpening filter: Sharpening filter can make the image after the previous filters look clearer and sharper, and enhance the details and features of the image. This paper uses the sharpening method of high-lift filtering, and the formula is as follows:

[0084] I sharpened (x,y)=I(x,y)+α·(I(x,y)-I blurred (x, y));

[0085] I(x, y) is the original image. blurred (x, y) is the image after low-pass filtering. α is the coefficient that controls the sharpening strength, usually α>1. The sharpening filter extracts the high-frequency components in the image by applying a high-pass filter, performs a second-order derivative of the image using the Laplacian operator, enhances the edges of the image, and adds the original image to the high-pass filtered image to enhance the details and edges of the image.

[0086] Among them, step 1 performs noise processing. By artificially adding a certain amount of noise (such as Gaussian noise, salt and pepper noise, etc.) to the image, the noise components in the image can be made more obvious and easier to identify. This makes the hybrid filter show better denoising performance when facing real noise. Because by artificially adding noise, the hybrid filter has learned how to identify and process these noise components during the training process, so it can remove them more effectively in practical applications.

[0087] In hybrid filters, such as non-local mean filters, when removing noise, the similarity between pixels in the image is used to estimate and restore the original pixel values. By adding noise first, the diversity of pixel values ​​in the image can be increased, so that the hybrid filter can better adapt to different types of noise during application and enhance the robustness of the hybrid filter.

[0088] Among the hybrid filters, non-local mean filters can better protect the image edges when removing noise, and noise addition processing can further optimize the performance of the hybrid filters, so that they can better preserve the edge features of the image while removing noise.

[0089] The Dung Beetle Optimization (DBO) algorithm in step 3 is an optimization algorithm inspired by the behavior of dung beetles in nature. The algorithm includes rolling behavior, breeding behavior, foraging behavior, stealing behavior and dancing behavior.

[0090] Rolling behavior: In the algorithm, it is assumed that the intensity of the sun will also affect the path of the dung beetle. During the rolling process, the position of the dung beetle is updated as follows:

[0091]

[0092] Here x i (t+1) is the position of the i-th dung beetle at the t-th iteration, k∈(0,0.2] represents a constant value indicating the deflection coefficient, b∈(0,1) is a constant; α is a natural coefficient assigned to -1 or 1, X w It is used to indicate the global worst position, and Δx is used to indicate the degree of change in light intensity.

[0093] For α, it is set to 1 or -1 by a probability method, where α = 1 means that the natural environment does not affect the original direction, while α = -1 means that it deviates from the original direction. The higher the value of Δx, the weaker the light source, which encourages dung beetles to avoid this position, so that the entire search space is explored as thoroughly as possible during the optimization process and the possibility of falling into a local optimum is reduced.

[0094] Dancing behavior: When the dung beetle encounters an obstacle and cannot move forward, it needs to dance to locate itself and obtain a new route. In order to simulate dancing behavior, the tangent function is used to obtain the new rolling direction, which is in the range of [0, π]. Once the dung beetle successfully determines the new direction, it should continue to roll the dung ball backward. Therefore, the position update method of the dung beetle when rolling the dung ball is:

[0095] x i (t+1)=x i (t)+tan(θ)|x i (t)-x i (t-1)|;

[0096] |x i (t)-x i (t-1)| is the difference between the position of the i-th dung beetle at the t-th iteration and the position at the t-1-th iteration. Therefore, the position update of the rolling dung beetle is closely related to the current information and historical information. Here θ∈|0,π] is the deflection angle. Note that when θ is equal to 0,π / 2,π, the dung beetle will not change its position.

[0097] Reproductive behavior: When the dung ball is rolled back home, in order to provide a safe environment for the offspring, a suitable place for laying eggs is selected. A boundary selection strategy is introduced to simulate the area where the female insect lays eggs, which is defined as follows:

[0098] Lb * =max(X * ×(1-R), Lb), Ub * =min(X * ×(1+R),Ub);

[0099] Where Lb * , Ub * are the lower and upper limits of the spawning area, Lb and Ub are the lower and upper limits of the search space, X * is the optimal position in the current population, and the inertia weight R = 1-t / T max , where T max The maximum number of iterations during algorithm iteration.

[0100] Once the egg-laying area is determined, the female insect will choose the brooding dung ball in the area to lay eggs. For the DBO algorithm, each female insect will definitely produce only one egg in each iteration, that is, one solution. The boundary range of the egg-laying area changes dynamically, which can prevent the algorithm from falling into the local optimal value, which is mainly determined by the inertia weight R value. Therefore, the position of the brooding dung ball is also dynamic during the iteration process, and its definition is as follows:

[0101] B i (t+1)=X * +b1×(Bi (t)-Lb * )+b2×(B i (t)-Ub * );

[0102] Among them B i (t) is the position of the i-th brooding ball at the t-th iteration, b1 and b2 represent two independent random vectors of size 1×D, and D represents the dimension of the optimization problem. Note that the position of the brooding ball is strictly limited to a certain range, namely the spawning area.

[0103] Foraging behavior: When the young dung beetles hatch successfully, they will run out to forage in groups, so it is necessary to establish the best foraging areas to guide them.

[0104] Lb b =max(X b ×(1-R), Lb), Ub b =min(X b ×(1+R),Ub);

[0105] Where X b is the global optimal position, Lb b , Ub b are the lower and upper limits of the optimal foraging area. After determining the area, we can define the location update method of the dung beetle:

[0106] x i (t+1)=x i (t)+C1×(x i (t)-Lb b )+C2×(x i (t)-Ub b );

[0107] Here x i (t) represents the position information of the i-th dung beetle at the t-th iteration, C1 represents a random number following a normal distribution, and C2 represents a random vector belonging to (0,1).

[0108] Thieves: Some dung beetles, called thieves, steal dung balls from other dung beetles. b is the best food source. Assume that X b Left and right represent the best positions for fighting for food. During the iteration, the thief's position information is updated and can be described as follows:

[0109] x i (t+1)=X b +S×g×(|x i (t)-X*|+|x i (t)-X b |);

[0110] where x i (t) represents the position of the i-th thief in the t-th iteration, g is a random vector of size 1×D, which follows a normal distribution, S represents a constant, and X b Left and right represent the best position to compete for food, X * is the optimal position in the current population.

[0111] In order to verify the performance of this solution, this embodiment uses four brain slices of consistent quality but different types to compare the denoising effect. The brain slice data comes from brain MRI images published by Kaggle.

[0112] (Source: https: / / www.kaggle.com / datasets / ashfakyeafi / brain-mri-images).

[0113] This dataset has dimensions of 496×248, a bit depth of 24, a horizontal resolution of 100 dpi, and a vertical resolution of 100 dpi.

[0114] Multiple indicators are used for evaluation, including mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), edge preservation index (EPI), and ENR index.

[0115] The mean square error (MSE), which shows the difference between two images, is defined as:

[0116]

[0117] Among them, I0 is the original image without noise, I n is a noise image, k and l are the number of rows and columns respectively.

[0118] Peak signal-to-noise ratio (PSNR) is used to reflect the quality of the image estimated based on the original image. It gives the objective difference between the two images and can be defined as the ratio of the peak signal power to the average noise power:

[0119]

[0120] Where N is the number of gray levels in the image.

[0121] The Structural Similarity Index (SSIM), a computational metric used to understand the similarity between two images (x, y), is defined as:

[0122]

[0123] Where c1 and c2 are constants, μ xis the mean value of x, μ y is the mean value of y, and are the variances of x and y, σ xy represents the covariance of x and y.

[0124] The edge preservation index (EPI) is an indicator used to measure the degree of edge information preservation after image denoising or filtering. It is defined as:

[0125]

[0126] in, represents the edge strength of the original image at the i-th pixel, It represents the edge strength of the denoised or processed image at the i-th pixel, and n is the total number of all pixels in the image.

[0127] The ENR index is used to measure the relationship between the edge information and noise of an image. It is defined as:

[0128]

[0129]

[0130] in, and are the gradients of the image in the x and y directions respectively. m and n are the number of rows and columns of the image respectively. N is the total number of pixels in the image.

[0131] The image denoising algorithm proposed in this embodiment is referred to as GBA algorithm. The denoising effect of the GBA algorithm is compared with that of existing filters. The existing filters used for comparison are: three-dimensional block matching filter (BM3D), non-local mean filter (NLM), multi-delay block frequency domain adaptive filter (MDF, Multidelay Block Frequency DomainAdaptive Filter; a method of dividing the original multi-order filter into K equally divided sub-blocks, and performing adaptive filtering in each sub-block of length N), bilateral filter (BF), mean filter (MF), Wiener filter (WF, Wiener Filter; an optimal estimator based on the minimum mean square error criterion) and Gaussian filter (GF).

[0132] 1. Speckle Noise Image Comparison

[0133] The brain MRI images of the central axial slices of the brain were selected to add speckle noise, and the GBA algorithm and the existing filter were used to perform noise reduction. The denoising results are shown in the attached figure. Figure 4As shown, (a) is the original MRI image, (b) is the image after adding speckle noise, (c) is the denoised image generated by GBA, (d) is the denoised image generated by BM3D, (e) is the denoised image generated by NLM, (f) is the denoised image generated by MDF, (g) is the denoised image generated by BF, (h) is the denoised image generated by MF, (i) is the denoised image generated by WF, and (j) is the denoised image generated by GF. The small rectangle that enlarges the ROI shows the large matrix part.

[0134] GBA can effectively preserve the edges and details of the image while suppressing speckle noise, showing excellent visual effects. In contrast, existing filtering algorithms are relatively rough in contour processing, often resulting in excessive smoothing of noise areas, which in turn affects the preservation of edge structures.

[0135] Table 1 shows the performance comparison of various filtering methods under speckle noise.

[0136]

[0137]

[0138] As shown in Table 1, the GBA algorithm achieved a PSNR improvement of 1.28, a SSIM improvement of 0.1416, a MSE reduction of 0.0005, an ENR improvement of 6.69, and an EPI improvement of 4.5705, which indicates that it has superior image quality, edge information, and structural similarity compared to other algorithms.

[0139] 2. Poisson Noise Image Comparison

[0140] The brain MRI images of the coronal slices of the brain were selected to add Poisson noise, and the GBA algorithm and the existing filter were used to perform noise reduction. The denoising results are shown in the attached figure. Figure 5 As shown, (a) is the original MRI image, (b) is the image after adding Poisson noise, (c) is the denoised image generated by GBA, (d) is the denoised image generated by BM3D, (e) is the denoised image generated by NLM, (f) is the denoised image generated by MDF, (g) is the denoised image generated by BF, (h) is the denoised image generated by MF, (i) is the denoised image generated by WF, and (j) is the denoised image generated by GF. The small rectangle of the enlarged ROI shows the large matrix part; the area indicated by the circle and arrow is the focus area.

[0141] GBA overall shows better Poisson noise removal effect than traditional methods. Compared with existing filtering methods, the GBA algorithm can clearly retain texture features and enhance edge information when suppressing Poisson noise in images. Figure 5As shown in the circle area indicated by the middle arrow, GBA performs well in noise suppression and contour detail preservation, significantly outperforming other algorithms, fully demonstrating its superiority in image processing.

[0142] Table 2 shows the performance comparison of various filtering methods under Poisson noise.

[0143] Metrics GBA BM3D NLM MDF BF MF WF GF PSNR 26.07 23.90 22.17 15.14 17.14 20.56 17.7 21.79 SSIM 0.8028 0.6949 0.7762 0.3974 0.5004 0.5996 0.5954 0.6951 MSE 0.0036 0.0042 0.0061 0.0306 0.0193 0.0088 0.017 0.0066 ENR 9.3954 6.8811 5.7370 1.1176 1.7792 3.9010 2.0165 5.2625 EPI 11.4858 4.4288 2.1489 2.4154 2.6592 6.6250 3.9804 8.2830

[0144] The data comparison is shown in Table 2. The GBA algorithm achieves a PSNR improvement of 2.17, a SSIM improvement of 0.1079, a MSE reduction of 0.0006, an ENR improvement of 2.51, and an EPI improvement of 3.2028. This indicates a good ability to preserve edge information, resulting in relatively low noise levels and overall excellent image quality.

[0145] 3. Salt and Pepper Noise Image Comparison

[0146] The brain MRI images of the central slice were selected to add salt and pepper noise, and the GBA algorithm and the existing filter were used to perform noise reduction. The denoising results are shown in the attached figure. Figure 6 As shown in the figure, (a) is the original MRI image, (b) is the image after adding salt and pepper noise, (c) is the denoised image generated by GBA, (d) is the denoised image generated by BM3D, (e) is the denoised image generated by NLM, (f) is the denoised image generated by MDF, (g) is the denoised image generated by BF, (h) is the denoised image generated by MF, (i) is the denoised image generated by WF, and (j) is the denoised image generated by GF.

[0147] The BM3D, NLM, BF, MF, WF and GF algorithms showed similar performance in removing salt and pepper noise. Although the MDF algorithm performed well in processing salt and pepper noise, some subtle noises were not completely removed, which may affect the medical staff's accurate judgment of the lesions in clinical diagnosis. In contrast, the GBA algorithm, based on MDF, performed a more in-depth denoising process on the entire brain area, which not only effectively retained the contours and details, but also greatly eliminated the cluttered signals. This improvement can significantly enhance the medical staff's ability to identify lesions and better understand the patient's condition.

[0148] Table 3 shows the performance comparison of various filtering methods under salt and pepper noise

[0149] Metrics GBA BM3D NLM MDF BF MF WF GF PSNR 26.41 16.27 15.25 17.95 8.7 13.87 14.55 14.41 SSIM 0.7993 0.2534 0.3674 0.8061 0.0541 0.1864 0.3256 0.2827 MSE 0.0024 0.0240 0.0299 0.0160 0.1350 0.0411 0.0350 0.0362 ENR 14.1934 1.187 1.1479 2.0932 0.2187 0.8567 0.9762 0.9685 EPI 9.2264 0.7432 0.5662 4.0428 1.7794 2.5710 1.9767 2.6669

[0150] Through data comparison as shown in Table 3, PSNR increased by 8.46, MSE decreased by 0.0136, ENR increased by 11.2614, EPI increased by 5.1836, but the SSIM index decreased by 0.0068 compared with MDF. Although the MDF algorithm is slightly less effective in removing salt and pepper noise, it has certain advantages in retaining the details of the original image.

[0151] 4. Gaussian Noise Image Comparison

[0152] The brain MRI images of the central axial slices were selected to add Gaussian noise, and the GBA algorithm and the existing filter were used to perform noise reduction. The denoising results are shown in the attached figure. Figure 7 As shown, (a) is the original MRI image, (b) is the image after adding Gaussian noise, (c) is the denoised image generated by GBA, (d) is the denoised image generated by BM3D, (e) is the denoised image generated by NLM, (f) is the denoised image generated by MDF, (g) is the denoised image generated by BF, (h) is the denoised image generated by MF, (i) is the denoised image generated by WF, and (j) is the denoised image generated by GF. The small rectangle that enlarges the ROI shows the large matrix part.

[0153] The edge details and spatial structures of images in the processing of Gaussian noise by BM3D, NLM, BF, MF, WF and GF algorithms are not much different. Although the denoising ability of BM3D algorithm is slightly improved compared with traditional methods, its performance in dealing with Gaussian noise is not as good as that of GBA algorithm. Obviously, GBA algorithm has shown significant advantages in suppressing Gaussian noise, while being able to effectively preserve the edge details in the image.

[0154] Table 4 shows the performance comparison of various filtering methods under Gaussian noise

[0155] Metrics GBA BM3D NLM MDF BF MF WF GF PSNR 23.51 20.32 19.74 18.95 17.76 18.30 18.60 19.01 SSIM 0.5679 0.3822 0.4391 0.3667 0.2622 0.3258 0.3612 0.3985 MSE 0.0045 0.0094 0.0106 0.0127 0.0167 0.0148 0.0138 0.0126 ENR 7.3414 3.0600 3.3236 2.7834 2.0811 1.8931 2.0253 2.2346 EPI 9.2723 2.0816 2.2197 7.3521 3.2060 3.745 3.4477 4.5280

[0156] The data comparison is shown in Table 4. PSNR is improved by 3.19, SSIM is improved by 0.1288, MSE is reduced by 0.0049, ENR is improved by 4.2814, and EPI is improved by 1.9202. In terms of processing Gaussian noise, the GBA algorithm outperforms other algorithms in various indicators and performs well in overall detail and edge preservation.

[0157] The GBA algorithm demonstrates effective performance improvement by combining 3D chaotic mapping and dung beetle optimization algorithm. The search space is further expanded under 3D chaotic mapping, and the parameters are iteratively optimized to effectively retain features during the filtering process. From the above noise reduction effect comparison, the GBA algorithm is more effective than other existing filtering algorithms in maintaining specific features. The GBA algorithm improves the quality of medical images by reducing noise, artifacts and other defects to effectively help medical staff analyze lesions and make more accurate judgments. The 3D chaotic mapping algorithm effectively ensures the uniformity of the population when initializing the dung beetle population and effectively reduces the convergence time, thereby reducing the computational burden, speeding up the processing time, and helping to reduce data redundancy, thereby simplifying the storage and transmission of medical images.

[0158] The above is only an embodiment of the present invention, and the common knowledge such as the known specific technical solutions and / or characteristics in the solution is not described in detail here. It should be pointed out that for those skilled in the art, without departing from the technical solution of the present invention, several modifications and improvements can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A medical image denoising method based on 3D chaotic mapping dung beetle algorithm, characterized in that: The following steps are involved: Step 1, inputting an original medical image to be processed, performing noise processing on the original medical image, and generating a noisy image; Step 2, inputting the noisy image into a hybrid filter for denoising the image; Step 3, using the dung beetle optimization algorithm to calculate the optimal parameter combination of each filter in the hybrid filter, and using the optimal parameter combination to configure the hybrid filter; Step 4, using the configured hybrid filter to denoise the noisy image to generate a denoised image; Among them, in step 3, the distribution position of the population is initialized using 3D chaotic mapping, and the 3D chaotic mapping is: Among them, x, y, z are system state variables, a=-0.6, c=-10, 1≤b≤7, and the initial values ​​are set to (0.1, 0, 0).

2. The medical image denoising method based on 3D chaotic mapping dung beetle algorithm according to claim 1, characterized in that: In step 3, the optimal value of b is 3.

7.

3. The medical image denoising method based on 3D chaotic mapping dung beetle algorithm according to claim 2, characterized in that: In step 2, the hybrid filter includes: a Gaussian filter, a median filter, a mean filter, a non-local mean filter and a sharpening filter.

4. The medical image denoising method based on 3D chaotic mapping dung beetle algorithm according to claim 3, characterized in that: The dung beetle algorithm includes a rolling ball behavior. During the rolling behavior, the calculation formula for updating the dung beetle position is: x i (t+1) is the position of the i-th dung beetle at the t-th iteration, k∈(0,0.2] is a constant value indicating the deflection coefficient, b∈(0,1) is a constant; α is -1 or 1, X w is the global worst position, Δx is the degree of change in light intensity; When α=1, it indicates that the natural environment does not affect the original direction, and when α=-1, it indicates that the direction deviates from the original direction.

5. The medical image denoising method based on 3D chaotic mapping dung beetle algorithm according to claim 4, characterized in that: The dung beetle algorithm includes dancing behavior. During the dancing behavior, the calculation formula for updating the position of the dung beetle is: x i (t+1)=x i (t)+tan(θ)∣x i (t)-x i (t-1)∣? ∣x i (t)-x i (t-1)| is the difference between the position of the i-th dung beetle at the t-th iteration and the position at the t-1-th iteration; θ∈[0,π] is the deflection angle; when θ is equal to 0, π / 2, π, the dung beetle will not change its position.

6. The medical image denoising method based on 3D chaotic mapping dung beetle algorithm according to claim 5, characterized in that: The dung beetle algorithm includes a breeding behavior, in which the calculation formula for the egg-laying area is: Lb * =max(X * ×(1-R),Lb),Ub * =min(X * ×(1+R),Ub); Lb * , Ub * are the lower and upper limits of the spawning area, Lb and Ub are the lower and upper limits of the search space, X * is the optimal position in the current population, and the inertia weight R = 1-t / T max , T max It is the maximum number of iterations during algorithm iteration; The calculation formula for the position of the incubation fecal ball is: B i (t+1)=X * +b1×(B i (t)-Lb * )+b2×(B i (t)-Ub * ); B i (t) is the position of the i-th incubation ball at the t-th iteration, b1 and b2 represent two independent random vectors of size 1×D, and D is the dimension of the optimization problem.

7. The medical image denoising method based on 3D chaotic mapping dung beetle algorithm according to claim 6, characterized in that: The dung beetle algorithm includes foraging behavior. In the foraging behavior, the calculation formula of the best foraging area is: Lb b =max(X b ×(1-R),Lb),Ub b =min(X b ×(1+R),Ub); X b is the global optimal position, Lb b ,Ub b are the lower and upper limits of the optimal foraging area; The calculation formula for the position update of the dung beetle is: x i (t+1)=x i (t)+C1×(x i (t)-Lb b )+C2×(x i (t)-Ub b ); x i (t) represents the position information of the i-th dung beetle at the t-th iteration, C1 represents a random number following a normal distribution, and C2 represents a random vector belonging to (0,1).

8. The medical image denoising method based on 3D chaotic mapping dung beetle algorithm according to claim 7, characterized in that: The dung beetle algorithm includes stealing behavior. During the stealing behavior, the position update calculation formula of the thief dung beetle is: x i (t+1)=X b +S×g×(|x i (t)-X * |+|x i (t)-X b |); where x i (t) represents the position of the i-th thief in the t-th iteration, g is a random vector of size 1×D, which follows a normal distribution, S represents a constant, and X b To compete for the best position for food, X * is the optimal position in the current population.

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