scholarly journals Optimal Multilevel Thresholds based on Tsallis Entropy Method using Golden Ratio Particle Swarm Optimization for Improved Image Segmentation

2012 ◽  
Vol 30 ◽  
pp. 364-371 ◽  
Author(s):  
K Manikantan ◽  
Arun B V ◽  
Darshan Kumar S Yaradoni
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Golden Ratio ◽  
Particle Swarm ◽  
Entropy Method ◽  
Tsallis Entropy ◽  
Swarm Optimization

Multi-Threshold Image Segmentation Based on Improved Particle Swarm Optimization and Maximum Entropy Method

2014 ◽  
Vol 989-994 ◽  
pp. 3649-3653 ◽  
Author(s):  
Ya Ti Lu ◽  
Wen Li Zhao ◽  
Xiao Bo Mao
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Particle Swarm ◽  
Entropy Method ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Threshold Segmentation ◽  
Expansion Model

Both maximum entropy method and Particle swarm optimization (PSO) are common threshold segmentation methods which have been used not only in image segmentation, but also in multi-threshold segmentation. Maximum entropy method is time-consuming, PSO may easily get trapped in a local optimum. In view of this concerning issue, we propose the PSO and maximum entropy are combined to make improvements on the PSO introduced in expansion model and opposition-based module. The objective functions of the maximum entropy as well as the PSO are obtained, which have improved to optimize them and search the optimal threshold combination, to achieve multi-threshold image segmentation. The results demonstrate that the new algorithm improved the segmentation speed and enhanced the robustness. And the optimizing results are stable.

Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion

2019 ◽  
Vol 10 (3) ◽  
pp. 91-106
Author(s):  
Abdul Kayom Md Khairuzzaman ◽  
Saurabh Chaudhury
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Anisotropic Diffusion ◽  
Particle Swarm ◽  
Structural Similarity ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Mr Image ◽  
Swarm Optimization ◽  
Mr Image Segmentation

Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

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