FCM fuzzy clustering image segmentation algorithm based on fractional particle swarm optimization

Zhang, Le; An, Zhiyong

doi:10.3233/jifs-179580

Cited by 9 publications

(5 citation statements)

References 15 publications

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“…Compared with the two algorithms before mixing, the segmentation accuracy is higher. Literature [23] proposed an image segmentation algorithm based on PSO and FCM. Simulation experiments show that the fusion algorithm has better results in image segmentation.…”

Section: Related Researchmentioning

confidence: 99%

An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

Jiao

Chen

Zhang³

2021

Security and Communication Networks

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Image segmentation is an important part of image processing. For the disadvantages of image segmentation under multiple thresholds such as long time and poor quality, an improved cuckoo search (ICS) is proposed for multithreshold image segmentation strategy. Firstly, the image segmentation model based on the maximum entropy threshold is described, and secondly, the cuckoo algorithm is improved by using chaotic initialization population to improve the diversity of solutions, optimizing the step size factor to improve the possibility of obtaining the optimal solution, and using probability to reduce the complexity of the algorithm; finally, the maximum entropy threshold function in image segmentation is used as the individual fitness function of the cuckoo search algorithm for solving. The simulation experiments show that the algorithm has a good segmentation effect under four different thresholding conditions.

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Section: Related Researchmentioning

confidence: 99%

An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

Jiao

Chen

Zhang³

2021

Security and Communication Networks

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“…Step 3. Update c 1 and c 2 with asynchronous monotonically decreasing individual learning factor and asynchronous monotonically increasing social learning factor, c 1 (k) and c 2 (k), instead of Equations ( 10) and (11) to individual, thereby improving the accuracy of particle optimization. Calculate the fitness values for individual particles, updating the individual extremums of particles and the global extremums of particles.…”

Section: Our Methods and Processmentioning

confidence: 99%

“…In addition, Elaziz et al, in view of the shortcomings of the Harris Hawks Optimizer algorithm, proposed an improved version of the HHO algorithm, which solved the problem of poor search ability of traditional algorithms and makes it easy to fall into local optimization [10]. Zhang et al proposed an improved PSO algorithm to solve the problem of premature convergence of traditional PSO and effectively realize adaptive image segmentation [11]. Zhao et al proposed a cross strategy-based ant colony algorithm (CCACO) in order to solve the continuity problem of the ant colony algorithm, which uses Kapur entropy as the objective function for image segmentation.…”

Section: Introductionmentioning

confidence: 99%

OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm

et al. 2022

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In view of the slow convergence speed of traditional particle swarm optimization algorithms, which makes it easy to fall into local optimum, this paper proposes an OTSU multi-threshold image segmentation based on an improved particle swarm optimization algorithm. After the particle swarm completes the iterative update speed and position, the method of calculating particle contribution degree is used to obtain the approximate position and direction, which reduces the scope of particle search. At the same time, the asynchronous monotone increasing social learning factor and the asynchronous monotone decreasing individual learning factor are used to balance global and local search. Finally, chaos optimization is introduced to increase the diversity of the population to achieve OTSU multi-threshold image segmentation based on improved particle swarm optimization (IPSO). Twelve benchmark functions are selected to test the performance of the algorithm and are compared with the traditional meta-heuristic algorithm. The results show the robustness and superiority of the algorithm. The standard dataset images are used for multi-threshold image segmentation experiments, and some traditional meta-heuristic algorithms are selected to compare the calculation efficiency, peak signal to noise ratio (PSNR), structural similarity (SSIM), feature similarity (FSIM), and fitness value (FITNESS). The results show that the running time of this paper is 30% faster than other algorithms in general, and the accuracy is also better than other algorithms. Experiments show that the proposed algorithm can achieve higher segmentation accuracy and efficiency.

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“…For example, determining white matter (WM) and grey matter volume in brain magnetic resonance images (MRI) has become an important measurement tool for multiple sclerosis (MS) patient monitoring and research [3][4][5]. A number of different approaches for brain tissue segmentation have been identified in the literature, including histogram-based techniques, edge detection, regionbased segmentation [6,7], fuzzy clustering [8][9][10][11][12], graph cuts [13,14], genetic algorithms [15][16][17], threshold approaches [18][19][20][21], and hybrid techniques [22,23].…”

Section: Introductionmentioning

confidence: 99%

Fusion Strategy for Improving Medical Image Segmentation

Alraddady¹,

Zanaty²,

bakr³

et al. 2023

Computers, Materials &Amp; Continua

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In this paper, we combine decision fusion methods with four metaheuristic algorithms (Particle Swarm Optimization (PSO) algorithm, Cuckoo search algorithm, modification of Cuckoo Search (CS McCulloch) algorithm and Genetic algorithm) in order to improve the image segmentation. The proposed technique based on fusing the data from Particle Swarm Optimization (PSO), Cuckoo search, modification of Cuckoo Search (CS McCulloch) and Genetic algorithms are obtained for improving magnetic resonance images (MRIs) segmentation. Four algorithms are used to compute the accuracy of each method while the outputs are passed to fusion methods. In order to obtain parts of the points that determine similar membership values, we apply the different rules of incorporation for these groups. The proposed approach is applied to challenging applications: MRI images, gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical images. It is shown that the proposed method gives accurate results; due to the decision fusion produces the greatest improvement in classification accuracy.

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FCM fuzzy clustering image segmentation algorithm based on fractional particle swarm optimization

Cited by 9 publications

References 15 publications

An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

An Improved Cuckoo Search Algorithm for Multithreshold Image Segmentation

OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm

Fusion Strategy for Improving Medical Image Segmentation

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