A Novel Neutrosophic Image Segmentation Based on Improved Particle Swarm Optimization Fuzzy C-Means Algorithm

2019 ◽  
Author(s):  
Xiaoli Wang ◽  
Jing Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy C Means ◽  
Fuzzy C Means Algorithm

Fuzzy C-Means Clustering Algorithm for Image Segmentation Based on Improved Particle Swarm Optimization

2012 ◽  
Vol 532-533 ◽  
pp. 1553-1557 ◽  
Author(s):  
Yue Yang ◽  
Shu Xu Guo ◽  
Run Lan Tian ◽  
Peng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy C Means ◽  
Improved Pso

A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

Evaluating an Evolutionary Particle Swarm Optimization for Fast Fuzzy C-Means Clustering on Liver CT Images

2014 ◽  
pp. 1-21 ◽  
Author(s):  
Abder-Rahman Ali ◽  
Micael S. Couceiro ◽  
Ahmed M. Anter ◽  
Aboul Ella Hassanian
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Image Data ◽  
Ct Images ◽  
Swarm Optimization ◽  
Liver Ct ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Pair Wise Comparison ◽  
Fuzzy C Means Algorithm

An Evolutionary Particle Swarm Optimization based on the Fractional Order Darwinian method for optimizing a Fast Fuzzy C-Means algorithm is proposed. This chapter aims at enhancing the performance of Fast Fuzzy C-Means, both in terms of the overall solution and speed. To that end, the concept of fractional calculus is used to control the convergence rate of particles, wherein each one of them represents a set of cluster centers. The proposed solution, denoted as FODPSO-FFCM, is applied on liver CT images, and compared with Fast Fuzzy C-Means and PSOFFCM, using Jaccard Index and Dice Coefficient. The computational efficiency is achieved by using the histogram of the image intensities during the clustering process instead of the raw image data. The experimental results based on the Analysis of Variance (ANOVA) technique and multiple pair-wise comparison show that the proposed algorithm is fast, accurate, and less time consuming.

Fast Multilevel Thresholding Method for Image Segmentation Based on Improved Particle Swarm Optimization and Maximal Variance

2012 ◽  
Vol 532-533 ◽  
pp. 1741-1746 ◽  
Author(s):  
Zheng Tao Peng ◽  
Kang Ling Fang ◽  
Zhi Qi Su ◽  
Shi Hong Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Particle Swarm ◽  
Multilevel Thresholding ◽  
Object Function ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Thresholding Method ◽  
Optimal Thresholds

To determine the optimal thresholds in image segmentation, a new multilevel thresholding method based on improved particle swarm optimization (IPSO) is proposed in this paper. Firstly, use the conception of independent peaks to divide the histogram to several regions, secondly, the optimization object function using maximum between-class variance (MV) method can be gotten in each area, by the non-uniform mutation and Geese-LDW PSO optimization of the object function, the optimal thresholds can be gotten, and the image can be segmented with the thresholds. Compared with the basic MV algorithm and genetic algorithm (GA) modified MV, the experimental results show that the new method not only realizes the image segmentation well, but also improves the speed.

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