Medical Image Segmentation using Particle Swarm Optimization

被引：0

作者：

Ait-Aoudia, Samy ^{[1
]}

Guerrout, El-Hachemi ^{[1
]}

Mahiou, Ramdane ^{[1
]}

机构：

[1] ESI Ecole Natl Super Informat, Algiers 16270, Algeria

来源：

2014 18TH INTERNATIONAL CONFERENCE ON INFORMATION VISUALISATION (IV) | 2014年

关键词：

Medical image segmentation; Hidden Markov Random Field; Swarm Particles Optimization; Kappa Index;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 Kmeans and threshold based techniques.

引用

页码：287 / 291

页数：5

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