Image segmentation is a method of delineating prominent structures in an input image for post
processing such as texture based analysis, feature extraction, selection, and classification. Various medical
applications rely on efficient image segmentation algorithms for disease diagnosis and management.
In this work, autonomous segmentation of brain regions of Magnetic Resonance Images (MRI) is attempted
for neuroimaging applications. It is carried out by combining heuristic based image segmentation
with stochastic modeling. Initially, Particle Swarm Optimization (PSO) algorithm is used to identify ideal intensity thresholds. Labeling
priors from PSO are derived for the Markov random field. Markov Random Field (MRF), a probabilistic approach is used to
improve the partitioning of the initial segmented image obtained by the multilevel threshold technique. The efficacy of the segmentation
procedure is improved by refining the obtained apriori information by Maximum a posteriori (MAP) estimation of the MRFMAP
model. By this approach, the spatial information is incorporated into the segmentation process using MRF. A new metaheuristic
MRF image segmentation technique is proposed here to take advantage of both the methods. Performance assessment of
the proposed method is carried out using a numerical metric that evaluates the silhouette index of the estimated clusters. Experiments
conducted on different MRI datasets show the proposed methodology produces an average improvement in cluster classification
of 4.42% in terms of silhouette index for clinical datasets.