CN112085705B - Image segmentation method and device based on improved goblet sea squirt swarm algorithm - Google Patents
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract
The invention provides an image segmentation method based on an improved goblet-sea squirt swarm algorithm, which comprises the steps of inputting an image and initializing; generating a gray image by using the original image, and generating a non-local mean filtering image by combining a non-local mean filtering technology; constructing a corresponding two-dimensional histogram according to the gray level image and the non-local mean value filtering image, taking Kapur entropy as an objective function based on the constructed two-dimensional histogram, searching an optimal segmentation threshold by utilizing a goblet sea squirt swarm algorithm based on a skeleton random fractal search mechanism, segmenting the image based on the obtained optimal threshold, and outputting a segmentation result. By implementing the method and the device, the problems of the existing image segmentation algorithm, such as sinking into a local optimal solution, slow convergence speed, poor segmentation quality and the like, can be solved.
Description
Technical Field
The invention relates to the technical field of computer image processing, in particular to an image segmentation method and device based on an improved goblet sea squirt swarm algorithm.
Background
The image segmentation plays a role in assisting the medical diagnosis field, particularly the related diseases of the kidney, and the method with good segmentation effect can greatly promote the medical diagnosis accuracy and improve the diagnosis efficiency.
Multi-threshold segmentation is an efficient and simple segmentation method in the field of image segmentation, and is centered on the determination of the optimal threshold. The quality of the segmentation threshold can directly determine the segmentation quality of the image, and it is known that although the traditional gradient method can obtain the segmentation threshold, the method has larger calculation complexity and the obtained segmentation threshold has poorer quality. The group intelligent optimization algorithm is applied to multi-threshold image segmentation, and is an efficient method with good segmentation effect. However, due to the shortages of the group intelligent optimization method, the segmentation process is easily trapped in local optimization, so that the segmentation quality is poor.
Therefore, in order to avoid the local optimum in the segmentation process as much as possible and obtain better segmentation quality, it is necessary to provide an image segmentation method based on the improved goblet-sea squirt swarm algorithm, which can solve the problems of the existing image segmentation algorithm such as the local optimum solution, slow convergence speed, poor segmentation quality and the like.
Disclosure of Invention
The technical problem to be solved by the embodiment of the invention is to provide the image segmentation method and the device based on the improved goblet sea squirt swarm algorithm, which can solve the problems of the existing image segmentation algorithm such as sinking into a local optimal solution, slow convergence speed, poor segmentation quality and the like, and achieve the purposes of high operation efficiency and good segmentation effect.
In order to solve the technical problems, the embodiment of the invention provides an image segmentation method based on an improved goblet sea squirt swarm algorithm, which is characterized by comprising the following steps:
step S1: inputting an image to be segmented, and initializing a threshold segmentation level;
step S2: graying an input image to obtain a corresponding gray image, and obtaining a non-local mean value image by using a non-local mean value filtering mode;
step S3: constructing a corresponding two-dimensional histogram according to the gray level image and the non-local mean value filtering image, and taking Kapur entropy as an objective function based on the constructed two-dimensional histogram;
step S4: searching an optimal segmentation threshold by utilizing a goblet-sea squirt swarm algorithm based on a skeleton random fractal search mechanism, segmenting an image based on the obtained optimal threshold, and outputting a segmentation result.
The step S4 specifically includes:
step S41: initializing parameters including the population size pop ize of the sea squirt group, and the maximum Iteration number max_iteration;
step S42: calculating each individual P in the population i The fitness of (a), namely Kapur entropy, and finding the global optimum BP;
step S43: and performing a diffusion process: in the formula (1) and the formula (2), a Gaussian walk mode is selected to create the position of each new individual, and the optimal individual in the population is found out;
GW 1 ＝Gaussian(μ BP ,σ)+(ε×BP-ε′×P i ) (1)
GW 2 ＝Gaussian(μ P ,σ) (2)
wherein ε and ε' are in the interval [0, 1]]Random numbers obeying uniform distribution; BP and P i The positions of the optimal individual and the ith individual are respectively represented; mu (mu) BP 、μ P And sigma are both Gaussian parameters, and μ BP Equal to |BP|, μ P Equal to |P i I (I); the standard deviation sigma is calculated by the formula (3),step size used to reduce gaussian jump:
step S44: performing a first update operation: ranking all individuals according to the fitness value, and calculating each individual P according to formula (4) i Set performance level Pa of (1) i The method comprises the steps of carrying out a first treatment on the surface of the Wherein for each individual (P i ) Judging Pa i <Whether epsilon is satisfied; if so, updating each individual P according to equation (5) i Is the j-th component of (2); otherwise, remain unchanged;
P′ i (j)＝P r (j)-ε×(P t (j)-P i (j)) (5)
wherein Rank (P i ) For individual P i Ranking in a population; n is the size of the population; p'. i (j) For individual P i Updated position; p (P) r And P t Is a randomly selected individual from a population; epsilon is the interval 0,1]Random numbers obeying uniform distribution;
step S45: performing a second update operation: ranking the updated population of individuals according to the fitness value and calculating each individual P 'according to equation (4)' i Set performance level Pa of (1) i The method comprises the steps of carrying out a first treatment on the surface of the Wherein, for each individual P' i Judging Pa i <Whether epsilon is satisfied; if so, updating P 'according to formula (6)' i And if P i Has a fitness higher than P' i For the adaptation degree of (1), P i Substitution P' i The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, remain unchanged;
wherein P' r And P' t Two individuals randomly selected from the population generated in step S42;is a random number generated by gaussian normal distribution; ε' is the interval [0, 1]]Random numbers obeying uniform distribution;
step S46: checking whether the iteration termination condition is reached, and executing step S47 if the iteration termination condition is reached; otherwise, returning to the step S43 to continue iteration;
step S47: returning the optimal sea squirt position and fitness value to the optimal threshold and the maximum Kapur entropy;
step S48: and dividing the image by using the obtained optimal threshold value.
The embodiment of the invention also provides an image segmentation device based on the improved goblet sea squirt swarm algorithm, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the image segmentation method based on the improved goblet sea squirt swarm algorithm when executing the computer program.
The embodiment of the invention has the following beneficial effects:
1. the invention integrates a Gaussian backbone mechanism and a fractal search mechanism and is used for improving the global search capability and the local search capability of the goblet-sea squirt swarm algorithm based on the skeleton random fractal search mechanism, so that the proposed global search capability and the local search capability of the goblet-sea squirt swarm algorithm based on the skeleton random fractal search mechanism reach a balanced state, thereby solving the problems of the existing image segmentation algorithm such as sinking into a local optimal solution, slow convergence speed, poor segmentation quality and the like, and achieving the purposes of high operation efficiency and good segmentation effect;
2. the segmentation quality of the invention is superior to the original sea Squirt Swarm Algorithm (SSA) and other similar methods, such as Whale Optimization Algorithm (WOA), bat Algorithm (BA), sine Cosine Algorithm (SCA) and gray wolf optimization algorithm (GWO).
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are required in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that it is within the scope of the invention to one skilled in the art to obtain other drawings from these drawings without inventive faculty.
FIG. 1 is a flowchart of an image segmentation method based on an improved sea squirt swarm algorithm according to an embodiment of the present invention;
fig. 2 is a plan view of a two-dimensional histogram in an image segmentation method based on the modified ecteinascidia group algorithm according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present invention more apparent.
As shown in fig. 1, in a first embodiment of the present invention, an image segmentation method based on an improved ecteinascidial group algorithm is provided, and the method includes the following steps:
step S1: inputting an image to be segmented, and initializing a threshold segmentation level;
specifically, an image to be segmented (e.g., a kidney pathological image) is input, and a threshold segmentation Level is initialized.
Step S2: graying an input image to obtain a corresponding gray image, and obtaining a non-local mean value image by using a non-local mean value filtering mode;
firstly, carrying out graying treatment on an input image to obtain a gray image; and secondly, obtaining a non-local mean value image by using a non-local mean value filtering mode.
The non-local mean value is a new denoising technology which is proposed by Buades and the like in recent years, fully utilizes redundant information in an image, and maximally reflects the detail characteristics of the image while denoising. The basic idea is that the estimate of the current pixel is obtained by a weighted average of the values of pixels in the image that have a similar neighborhood structure. Assuming that I (p) and I (q) are gray values of pixels p and q in the image I, a non-local average value of the image I can be calculated from formulas (7) to (10).
Where O (p) is a filtered value of a non-local average, μ (p) and μ (q) are local averages, σ is a standard deviation, ω (p, q) is the weights of pixel p and pixel q, and L (p) and L (q) are m×m pixel blocks centered on pixel p and pixel q, respectively.
Step S3: constructing a corresponding two-dimensional histogram according to the gray level image and the non-local mean value filtering image, and taking Kapur entropy as an objective function based on the constructed two-dimensional histogram;
specifically, through the non-local mean image and the gray level image, a corresponding non-local mean two-dimensional histogram can be generated.
Assuming that the gray value level of the original gray image I (x, y) is [0, l-1] and the image size is mxn, the gray value level of the generated non-local mean image g (x, y) is also [0, l-1] and the image size is mxn. Then, a preliminary two-dimensional histogram can be formed from I (x, y) and g (x, y).
If the point (I, j) is formed by I (x, y) and g (x, y), where I represents the pixel value of a certain pixel in I (x, y) and j represents the pixel value of a certain pixel in g (x, y). H (i, j) represents the number of times point (i, j) appears on the gray value vector (s, t). Therefore, after normalization processing is performed, a final two-dimensional histogram can be obtained.
According to the definition of the non-local mean two-dimensional histogram described above, a corresponding two-dimensional plane histogram is given in FIG. 2, where { t } 1 ,t 2 …, L-1} represents the value of the gray scale image, { s 1 ,s 2 …, L-1} represents the value of the non-local mean image. In the two-dimensional histogram, the main diagonal contains the largest amount of image information, so that only Kapur entropy of n sub-regions on the main diagonal is calculated, which can be defined as formula (11).
Wherein,
thus, if Kapur entropy is used as the objective function, then the improved algorithm finds the pairMaximized threshold set { t } 1 ,t 2 ,…,t n-1 Then consider the optimal segmentation threshold.
Step S4: searching an optimal segmentation threshold by utilizing a goblet-sea squirt swarm algorithm based on a skeleton random fractal search mechanism, segmenting an image based on the obtained optimal threshold, and outputting a segmentation result.
Specifically, step S41: initializing parameters including the population size pop ize of the sea squirt group, and the maximum Iteration number max_iteration;
step S42: calculating each individual P in the population i The fitness of (a), namely Kapur entropy, and finding the global optimum BP;
step S43: and performing a diffusion process: in the formula (1) and the formula (2), a Gaussian walk mode is selected to create the position of each new individual, and the optimal individual in the population is found out;
GW 1 ＝Gaussian(μ BP ,σ)+(ε×BP-ε′×P i ) (1)
GW 2 ＝Gaussian(μ P ,σ) (2)
wherein ε and ε' are in the interval [0, 1]]Random numbers obeying uniform distribution; BP and P i The positions of the optimal individual and the ith individual are respectively represented; mu (mu) BP 、μ P And sigma are both Gaussian parameters, and μ BP Equal to |BP|, μ P Equal to |P i I (I); the standard deviation sigma is calculated by the formula (3),step size used to reduce gaussian jump:
step S44: performing a first update operation: ranking all individuals according to the fitness value, and calculating each individual P according to formula (4) i Set performance level Pa of (1) i The method comprises the steps of carrying out a first treatment on the surface of the Wherein for each individual (P i ) Judging Pa i <Whether epsilon is satisfied; if so, updating each individual P according to equation (5) i Is the j-th component of (2); otherwise, remain unchanged;
P′ i (j)＝P r (j)-ε×(P t (j)-P i (j)) (5)
wherein Rank (P i ) For individual P i Ranking in a population; n is the size of the population; p'. i (j) For individual P i Updated position; p (P) r And P t Is a randomly selected individual from a population; epsilon is the interval 0,1]Random numbers obeying uniform distribution;
step S45: performing a second update operation: ranking the updated population of individuals according to the fitness value and calculating each individual P 'according to equation (4)' i Set performance level Pa of (1) i The method comprises the steps of carrying out a first treatment on the surface of the Wherein, for each individual P' i Judging Pa i <Whether epsilon is satisfied; if so, updating P 'according to formula (6)' i And if P i Has a fitness higher than P' i For the adaptation degree of (1), P i Substitution P' i The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, remain unchanged;
wherein P' r And P' t Two individuals randomly selected from the population generated in step S42;is a random number generated by gaussian normal distribution; ε' is the interval [0, 1]]Random numbers obeying uniform distribution;
step S46: checking whether the iteration termination condition is reached, and executing step S47 if the iteration termination condition is reached; otherwise, returning to the step S43 to continue iteration;
step S47: returning the optimal sea squirt position and fitness value to the optimal threshold and the maximum Kapur entropy;
step S48: and dividing the image by using the obtained optimal threshold value.
Tables 1-3 show further analysis of peak signal to noise ratio (PSNR), structural Similarity (SSIM), feature Similarity (FSIM) evaluation results using Wilcoxon symbol Rank test, where "w" indicates that the segmentation effect of GBSFSSSA is superior to other methods, "t" indicates that the segmentation effect of GBSFSSSA is equal to other methods, "l" indicates that the segmentation effect of GBSFSSSA is inferior to other methods, "Mean" indicates the ranking average of each algorithm across all images, and "Rank" indicates the overall average ranking level of each algorithm. The GBSFSSSA is superior to other algorithms in the results of most images at different threshold levels, as observed by the analysis results of the Wilcoxon signed rank test. In addition, the sequencing mean value of the GBSFSSSA on all images under different threshold levels can be optimal, and the comprehensive mean value sequencing grade of the GBSFSSSA is No.1.
Wherein, the Wilcoxon sign rank test of PSNR evaluation results is shown in the following table 1:
wherein, wilcoxon sign rank test of SSIM evaluation result is shown in the following table 2:
wherein, wilcoxon signed rank test of FSIM evaluation results is shown in the following table 3:
in contrast to the image segmentation method based on the improved ascidian algorithm provided in the first embodiment of the present invention, the second embodiment of the present invention further provides an image segmentation device based on the improved ascidian algorithm, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the image segmentation method based on the improved ascidian algorithm in the first embodiment of the present invention when executing the computer program. It should be noted that, the process of executing the computer program by the processor in the second embodiment of the present invention is consistent with the execution process of each step in the image segmentation method based on the improved goblet-sea squirt swarm algorithm provided in the first embodiment of the present invention, and the description of the foregoing related content will be specifically referred to.
The embodiment of the invention has the following beneficial effects:
1. the invention integrates a Gaussian backbone mechanism and a fractal search mechanism and is used for improving the global search capability and the local search capability of the goblet-sea squirt swarm algorithm based on the skeleton random fractal search mechanism, so that the proposed global search capability and the local search capability of the goblet-sea squirt swarm algorithm based on the skeleton random fractal search mechanism reach a balanced state, thereby solving the problems of the existing image segmentation algorithm such as sinking into a local optimal solution, slow convergence speed, poor segmentation quality and the like, and achieving the purposes of high operation efficiency and good segmentation effect;
2. the segmentation quality of the invention is superior to the original sea Squirt Swarm Algorithm (SSA) and other similar methods, such as Whale Optimization Algorithm (WOA), bat Algorithm (BA), sine Cosine Algorithm (SCA) and gray wolf optimization algorithm (GWO).
Those of ordinary skill in the art will appreciate that all or a portion of the steps in implementing the methods of the above embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc.
The foregoing disclosure is illustrative of the present invention and is not to be construed as limiting the scope of the invention, which is defined by the appended claims.
Claims (2)
1. An image segmentation method based on an improved goblet sea squirt swarm algorithm is characterized by comprising the following steps:
step S1: inputting an image to be segmented, and initializing a threshold segmentation level;
step S2: graying an input image to obtain a corresponding gray image, and obtaining a non-local mean value image by using a non-local mean value filtering mode;
step S3: constructing a corresponding two-dimensional histogram according to the gray level image and the non-local mean value filtering image, and taking Kapur entropy as an objective function based on the constructed two-dimensional histogram;
step S4: searching an optimal segmentation threshold value by utilizing a goblet-sea squirt swarm algorithm based on a skeleton random fractal search mechanism, segmenting an image based on the obtained optimal threshold value, and outputting a segmentation result;
the step S4 specifically includes:
step S41: initializing parameters including the population size pop ize of the sea squirt group, and the maximum Iteration number max_iteration;
step S42: calculating each individual P in the population i The fitness of (a), namely Kapur entropy, and finding the global optimum BP;
step S43: and performing a diffusion process: in the formula (1) and the formula (2), a Gaussian walk mode is selected to create the position of each new individual, and the optimal individual in the population is found out;
GW 1 ＝Gaussian(μ BP ，σ)+(ε×BP-ε′×P i ) (1)
GW 2 ＝Gaussian(μ P ,σ) (2)
wherein ε and ε' are in the interval [0, 1]]Random numbers obeying uniform distribution; BP and P i The positions of the optimal individual and the ith individual are respectively represented; mu (mu) BP 、μ P And sigma are both Gaussian parameters, and μ BP Equal to |BP|, μ P Equal to |P i I (I); the standard deviation sigma is calculated by the formula (3),step size used to reduce gaussian jump:
step S44: performing a first update operation: ranking all individuals according to the fitness value, and calculating each individual P according to formula (4) i Set performance level Pa of (1) i The method comprises the steps of carrying out a first treatment on the surface of the Wherein for each individual (P i ) Judging Pa i <Whether epsilon is satisfied; if so, updating each individual P according to equation (5) i Is the j-th component of (2); otherwise, remain unchanged;
P′ i (j)＝P r (j)-ε×(P t (j)-P i (j) (5)
wherein Rank (P i ) For individual P i Ranking in a population; n is the size of the population; p'. i (j) For individual P i Updated position; p (P) r And P t Is a randomly selected individual from a population; epsilon is the interval 0,1]Random numbers obeying uniform distribution;
step S45: performing a second update operation: ranking the updated population of individuals according to the fitness value and calculating each individual P 'according to equation (4)' i Set performance level Pa of (1) i The method comprises the steps of carrying out a first treatment on the surface of the Wherein, for each individual P' i Judging Pa i <Whether epsilon is satisfied; if so, updating P 'according to formula (6)' i And if P i Has a fitness higher than P' i For the adaptation degree of (1), P i Substitution P' i The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, remain unchanged;
wherein P' r And P' t Two individuals randomly selected from the population generated in step S42;is a random number generated by gaussian normal distribution; ε' is the interval [0, 1]]Random numbers obeying uniform distribution;
step S46: checking whether the iteration termination condition is reached, and executing step S47 if the iteration termination condition is reached; otherwise, returning to the step S43 to continue iteration;
step S47: returning the optimal sea squirt position and fitness value to the optimal threshold and the maximum Kapur entropy;
step S48: and dividing the image by using the obtained optimal threshold value.
2. An image segmentation apparatus based on an improved ecteinascidity algorithm comprising a memory and a processor, the memory storing a computer program, wherein the processor, when executing the computer program, performs the steps of the image segmentation method based on an improved ecteinascidity algorithm as recited in claim 1.
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