Medical Image Segmentation Using Particle Swarm Optimization

Segmentation of medical images is one of the fundamental problems in image processing field. It aims to provide a crucial decision support to physicians. There are several methods to perform segmentation. Hidden Markov Random Fields (HMRF) constitutes an elegant way to model the problem of segmentation. This modelling leads to the minimization of an energy function. In this paper we focus on Particles Swarm Optimization (PSO) method to solve this optimization problem. The quality of segmentation is evaluated on grounds truths images using the Kappa index. The results show the supremacy of the HMRF-PSO method compared to K-means and threshold based techniques.