A population based hybrid FCM-PSO algorithm for clustering analysis and segmentation of brain image

Verma, Hanuman; Verma, Deepa; Tiwari, Pawan Kumar

doi:10.1016/j.eswa.2020.114121

Cited by 55 publications

(18 citation statements)

References 28 publications

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“…Note that Fuzzy C-means (FCM) tends to fall into local minima when facing complex problems. Verma et al [41] proposed hybrid FCM and particle swarm optimization (PSO) algorithms (Hybrid FCM-PSO), while the global optimization property of PSO is used to search for cluster centers. In [42], an Automatic Clustering Local Search HMS (ACLSHMS) algorithm was proposed for image segmentation, incorporating a local search operator in the algorithm aimed at optimizing the cluster configuration of the clusters.…”

Section: Literature Reviewmentioning

confidence: 99%

Gradient-based elephant herding optimization for cluster analysis

Duan

Liu

et al. 2022

Appl Intell

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Clustering analysis is essential for obtaining valuable information from a predetermined dataset. However, traditional clustering methods suffer from falling into local optima and an overdependence on the quality of the initial solution. Given these defects, a novel clustering method called gradient-based elephant herding optimization for cluster analysis (GBEHO) is proposed. A well-defined set of heuristics is introduced to select the initial centroids instead of selecting random initial points. Specifically, the elephant optimization algorithm (EHO) is combined with the gradient-based algorithm GBO for assigning initial cluster centers across the search space. Second, to overcome the imbalance between the original EHO exploration and exploitation, the initialized population is improved by introducing Gaussian chaos mapping. In addition, two operators, i.e., random wandering and variation operators, are set to adjust the location update strategy of the agents. Nine datasets from synthetic and real-world datasets are adopted to evaluate the effectiveness of the proposed algorithm and the other metaheuristic algorithms. The results show that the proposed algorithm ranks first among the 10 algorithms. It is also extensively compared with state-of-the-art techniques, and four evaluation criteria of accuracy rate, specificity, detection rate, and F-measure are used. The obtained results clearly indicate the excellent performance of GBEHO, while the stability is also more prominent.

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Section: Literature Reviewmentioning

confidence: 99%

Gradient-based elephant herding optimization for cluster analysis

Duan

Liu

et al. 2022

Appl Intell

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“…PSO has a wide range of modifications, which address specific aspects of the algorithm or the problem of interest. PSO can be improved by integrating other algorithms into its process-such as neural networks and support vector machines-or by modifying the fundamental rules for particle movement [4][5][6][7][8]. For relatively high dimensional problems, it is usually preferred to selectively optimize a subset of dimensions at a time [9].…”

Section: Related Workmentioning

confidence: 99%

“…followed by Equation (5). As with PSO, the individual's position update relies on Equation (4) subject to Equation (6). Percent noise injection-regulated by c 1 -is used as a precaution to improve variations in movement and increase the likelihood of escaping from local minima without causing a large change in course.…”

Section: Dimension-wise Psomentioning

confidence: 99%

Dimension-Wise Particle Swarm Optimization: Evaluation and Comparative Analysis

Schlauwitz

Musilek

2021

Applied Sciences

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This article evaluates a recently introduced algorithm that adjusts each dimension in particle swarm optimization semi-independently and compares it with the traditional particle swarm optimization. In addition, the comparison is extended to differential evolution and genetic algorithm. This presented comparative study provides a clear exposition of the effects introduced by the proposed algorithm. Performance of all evaluated optimizers is evaluated based on how well they perform in finding the global minima of 24 multi-dimensional benchmark functions, each having 7, 14, or 21 dimensions. Each algorithm is put through a session of self-tuning with 100 iterations to ensure convergence of their respective optimization parameters. The results confirm that the new variant is a significant improvement over the traditional algorithm. It also obtained notably better results than differential evolution when applied to problems with high-dimensional spaces relative to the number of available particles.

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“…Sambandam et al [ 15 ] proposed an adaptive dragonfly optimization approach for performing MIS of medical images. Verma et al [ 16 ] proposed a hybrid MIS method with Fuzzy c-means and the PSO algorithm, which was applied to execute experiments on publicly available real brain datasets. Radha et al [ 17 ] proposed a combination of an intelligent fuzzy level set approach and an improved quantum PSO for magnetic resonance image segmentation.…”

Section: Introductionmentioning

confidence: 99%

“… Sambandam et al [ 15 ] SADFO + Kapur’s entropy six medical images of eyes, liver, head and tongue It effectively optimized the threshold values by exploring the solution space. Verma et al [ 16 ] hybrid fuzzy c-means and PSO real brain datasets It improved up to 30% for real brain images. Radha et al [ 17 ] IQPSO + intelligent fuzzy level set method magnetic image resonance images It showed a promising significant improvement in the image segmentation process.…”

Section: Introductionmentioning

confidence: 99%

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

Liu

Zhao

et al. 2021

Computers in Biology and Medicine

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This paper focuses on the study of multilevel COVID-19 X-ray T image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present the first ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was succefully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com .

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A population based hybrid FCM-PSO algorithm for clustering analysis and segmentation of brain image

Cited by 55 publications

References 28 publications

Gradient-based elephant herding optimization for cluster analysis

Gradient-based elephant herding optimization for cluster analysis

Dimension-Wise Particle Swarm Optimization: Evaluation and Comparative Analysis

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation

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