Hidden Markov Random Fields and Particle
Swarm Combination for Brain
Image Segmentation
El-Hachemi Guerrout, Ramdane Mahiou, and Samy Ait-Aoudia
Laboratoire
des Méthodes de Conception des Systèmes-Ecole Nationale Supérieure en
Informatique, Algeria
Abstract: The interpretation of
brain images is a crucial task in the practitioners’ diagnosis process.
Segmentation is one of key operations to provide a decision support to
physicians. There are several methods to perform segmentation. We use Hidden
Markov Random Fields (HMRF) for modelling the segmentation problem. This
elegant model leads to an optimization problem. Particles Swarm Optimization
(PSO) method is used to achieve brain magnetic resonance image segmentation.
Setting the parameters of the HMRF-PSO method is a task in itself. We conduct a
study for the choice of parameters that give a good segmentation. The
segmentation quality is evaluated on ground-truth images, using the Dice
coefficient also called Kappa index. The results show a superiority of the
HMRF-PSO method, compared to methods such as Classical Markov Random Fields (MRF)
and MRF using variants of Ant Colony Optimization (ACO).
Keywords: Brain image
segmentation, hidden markov random field, swarm particles optimization, dice coefficient.