2-D entropy image segmentation on thresholding based on particle swarm optimization (PSO)
Molka Dhieb, Sabeur Masmoudi, Mohamed Ben Messaoud, Mondher Frikha, Faten Ben Arfia · 2014
Thresholding is one of the popular and fundamental techniques for conducting image segmentation. It is a widely used tool in image segmentation for extracting the object regions from their background. In this paper, image segmentation method based on two-dimensional histogram analysis through entropy maximization is presented. The 2-D maximum entropy threshold approach is proposed to segment a gray-scale image. To compensate for the weakness of the classical methods that may be trapped into the first entropy local maximum met, a new heuristic optimization algorithm, called the particle swarm optimization PSO is introduced. PSO algorithm is realized successfully in the process of solving the 2-D maximum entropy problem. Therefore, the convergence is improved and the reproducibility of the optimal solutions is better guaranteed. The experiments of segmenting images are illustrated to show that the proposed method can get ideal segmentation result.