Medical Image Segmentation Using Particle Swarm Optimization

Author

Ait-Aoudia, Samy ; Guerrout, El-Hachemi ; Mahiou, Ramdane

Author_Institution

ESI - Ecole Nat. Super. en Inf., Algiers, Algeria

fYear

2014

fDate

16-18 July 2014

Firstpage

287

Lastpage

291

Abstract

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.

Keywords

image segmentation; medical image processing; particle swarm optimisation; HMRF-PSO method; decision support; energy function; hidden Markov random fields; image processing field; medical image segmentation; particle swarm optimization; physicians; threshold based techniques; Biomedical imaging; Computational modeling; Hidden Markov models; Image segmentation; Indexes; Markov random fields; Particle swarm optimization; Hidden Markov Random Field; Kappa Index; Medical image segmentation; Swarm Particles Optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Visualisation (IV), 2014 18th International Conference on

Conference_Location

Paris

ISSN

1550-6037

Type

conf

DOI

10.1109/IV.2014.68

Filename

6902917