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.

scholarly journals A Multilevel Thresholding Approach Based on Improved Particle Swarm Optimization for Color Image Segmentation

2020 ◽  
Author(s):  
Larissa Britto ◽  
Luciano Pacífico ◽  
Teresa Ludermir
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Color Image ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Color Image Segmentation ◽  
Multilevel Thresholding ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Image Problems

In this paper, a hybrid Otsu and improved Particle Swarm Optimization (PSO) algorithm is presented to deal with multilevel color image thresholding problem, named APSOW. In APSOW, the historical information represented by the local best solutions found so far by PSO population are permuted among the current population, using a randomized greedy process. APSOW also implements a weedout operator to prune the worst individuals from the population. The proposed APSOW is compared to other hybrid EAs and Otsu approaches from literature (include standard PSO model) through twelve benchmark color image problems, showing its potential and robustness.

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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