scholarly journals 2-D Histogram based Multilevel Thresholding for Image Segmentation by Hybrid Bacterial Foraging Optimization and Particle Swarm Optimization

2019 ◽  
Vol 8 (10) ◽  
pp. 4404-4411
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Renyi Entropy ◽  
Rényi Entropy ◽  
Multilevel Thresholding ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Bacterial Foraging

The objective of image segmentation is to extract meaningful clusters in given image. Meaningful clusters are possible with perfect threshold values which are optimized by assuming Renyi entropy as an objective function. A 1-D histogram based multilevel thresholding is computationally complex and segmented image visual quality comparatively low because of equal distribution of energy over the entire histogram plan. To overcome the problem, a 2-D histogram based multilevel thresholding is proposed in this paper by maximizing the Renyi entropy with a novel Hybrid Bacterial Foraging Optimization Algorithm and Particle Swarm Optimization (hBFOA-PSO) and the obtained results are compared with state of art optimization techniques. The results of the proposed model have been evaluated on a standard image dataset. The results obtained after implementing a 2-D histogram suggest hBFOA-PSO can be effectively used won multilevel thresholding problems resulting in a high accuracy.

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.

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.

Bacterial Foraging Algorithm Based on Quantum-Behaved Particle Swarm Optimization for Global Optimization

2013 ◽  
Vol 655-657 ◽  
pp. 948-954 ◽  
Author(s):  
Ling Li ◽  
Xiong Fa Mai
Keyword(s):  
Global Optimization ◽  
Particle Swarm Optimization ◽  
Local Minimum ◽  
Random Search ◽  
Particle Swarm ◽  
Bacterial Foraging Optimization ◽  
Swarm Optimization ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm ◽  
Simulation Results

Bacterial Foraging Optimization(BFA) algorithm has recently emerged as a very powerful technique for real parameter optimization,but the E.coli algorithm depends on random search directions which may lead to delay in reaching the global solution.The quantum-behaved particle swarm optimization (QPSO) algorithm may lead to possible entrapment in local minimum solutions. In order to overcome the delay in optimization and to further enhance the performance of BFA,a bacterial foraging algorithm based on QPSO(QPSO-BFA) is presented.The new algorithm is proposed to combines both algorithms’ advantages in order to get better optimization values. Simulation results on eight benchmark functions show that the proposed algorithm is superior to the BFA,QPSO and BF-PSO.

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