Fuzzy entropy-based MR brain image segmentation using modified particle swarm optimization

2013 ◽  
Vol 23 (4) ◽  
pp. 281-288 ◽  
Author(s):  
R. Krishna Priya ◽  
C. Thangaraj ◽  
C. Kesavadas ◽  
S. Kannan
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Particle Swarm ◽  
Fuzzy Entropy ◽  
Swarm Optimization ◽  
Brain Image ◽  
Modified Particle Swarm Optimization ◽  
Mr Brain ◽  
Brain Image Segmentation

Multilevel Image Segmentation Using Modified Particle Swarm Optimization

2017 ◽  
pp. 106-142 ◽  
Author(s):  
Sourav De ◽  
Firoj Haque
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Optimization Technique ◽  
Real Life ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gray Scale ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Segmented Images

Particle Swarm Optimization (PSO) is a well-known swarm optimization technique. PSO is very efficient to optimize the image segmentation problem. PSO algorithm have some drawbacks as the possible solutions may follow the global best solution at one stage. As a result, the probable solutions may bound within that locally optimized solutions. The proposed chapter tries to get over the drawback of the PSO algorithm and proposes a Modified Particle Swarm Optimization (MfPSO) algorithm to segment the multilevel images. The proposed method is compared with the original PSO algorithm and the renowned k-means algorithm. Comparison of the above mentioned existing methods with the proposed method are applied on three real life multilevel gray scale images. For this purpose, three standard objective functions are applied to evaluate the quality of the segmented images. The comparison shows that the proposed MfPSO algorithm is done better than the PSO algorithm and the k-means algorithm to segment the real life multilevel gray scale images.

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