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.

Modified and Optimized Method for Segmenting Pulmonary Parenchyma in CT Lung Images, Based on Fractional Calculus and Natural Selection

2017 ◽  
Vol 28 (5) ◽  
pp. 721-732 ◽  
Author(s):  
S. Pramod Kumar ◽  
Mrityunjaya V. Latte
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Particle Swarm ◽  
Disease Diagnosis ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Otsu Method ◽  
Speed Up ◽  
Pulmonary Parenchyma ◽  
Aided Diagnosis

Abstract Computer-aided diagnosis of lung segmentation is the fundamental requirement to diagnose lung diseases. In this paper, a two-dimensional (2D) Otsu algorithm by Darwinian particle swarm optimization (DPSO) and fractional-order Darwinian particle swarm optimization (FODPSO) is proposed to segment the pulmonary parenchyma from the lung image obtained through computed tomography (CT) scans. The proposed method extracts pulmonary parenchyma from multi-sliced CT. This is a preprocessing step to identify pulmonary diseases such as emphysema, tumor, and lung cancer. Image segmentation plays a significant role in automated pulmonary disease diagnosis. In traditional 2D Otsu, exhaustive search plays an important role in image segmentation. However, the main disadvantage of the 2D Otsu method is its complex computation and processing time. In this paper, the 2D Otsu method optimized by DPSO and FODPSO is developed to reduce complex computations and time. The efficient segmentation is very important in object classification and detection. The particle swarm optimization (PSO) method is widely used to speed up the computation and maintain the same efficiency. In the proposed algorithm, the limitation of PSO of getting trapped in local optimum solutions is overcome. The segmentation technique is assessed and equated with the traditional 2D Otsu method. The test results demonstrate that the proposed strategy gives better results. The algorithm is tested on the Lung Image Database Consortium image collections.

Fuzzy C-Means Clustering Algorithm for Image Segmentation Based on Improved Particle Swarm Optimization

2012 ◽  
Vol 532-533 ◽  
pp. 1553-1557 ◽  
Author(s):  
Yue Yang ◽  
Shu Xu Guo ◽  
Run Lan Tian ◽  
Peng Liu
Keyword(s):  
Particle Swarm Optimization ◽  
Image Segmentation ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Fuzzy C Means ◽  
Improved Pso

A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

Optimization of Maximum Entropy Model Oriented to Bayes Prior Distribution Based on PSO

2010 ◽  
Vol 37-38 ◽  
pp. 814-818
Author(s):  
Yi Dai ◽  
Zhong Min Wang ◽  
Guang Ling
Keyword(s):  
Particle Swarm Optimization ◽  
Maximum Entropy ◽  
Prior Distribution ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Entropy Model ◽  
Swarm Optimization ◽  
Set Up ◽  
Searching Ability

How to acquire prior distribution is a key to Bayes method. Firstly, a nonlinear constrained optimal model of probability density function based on the principle of maximum entropy is set up. By using Lagrange multiplier this constrained optimal problem is transformed to a non-constrained optimal one, which is solved by standard particle swarm optimization (PSO) algorithm. Secondly, a new improved particle swarm optimization (IPSO) algorithm is proposed because standard PSO is slow on convergence and easy to be trapped in local optimum. IPSO introduces the hybrid method from genetic algorithm (GA) so that the overall searching ability is enhanced; then linear decreasing inertia weight is used to optimize particles. The simulation examples show that IPSO is simple and effective and it can rapidly converge with high quality solutions.

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