scholarly journals Application of Fuzzy C-Means Clustering Algorithm Based on Particle Swarm Optimization in Computer Forensics

2012 ◽  
Vol 24 ◽  
pp. 1186-1191 ◽  
Author(s):  
Deguang Wang ◽  
Baochang Han ◽  
Ming Huang
Keyword(s):  
Particle Swarm Optimization ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Computer Forensics ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals A image segmentation algorithm based on differential evolution particle swarm optimization fuzzy c-means clustering

2015 ◽  
Vol 12 (2) ◽  
pp. 873-893 ◽  
Author(s):  
Jiansheng Liu ◽  
Shangping Qiao
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Differential Evolution ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Color Image Segmentation ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

This paper presents a hybrid differential evolution, particle swarm optimization and fuzzy c-means clustering algorithm called DEPSO-FCM for image segmentation. By the use of the differential evolution (DE) algorithm and particle swarm optimization to solve the FCM image segmentation influenced by the initial cluster centers and easily into a local optimum. Empirical results show that the proposed DEPSO-FCM has strong anti-noise ability; it can improve FCM and get better image segmentation results. In particular, for the HSI color image segmentation, the DEPSO-FCM can effectively solve the instability of FCM and the error split because of the singularity of the H component.

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.

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