Hidden Markov random field model based brain MR image segmentation using clonal selection algorithm and Markov chain Monte Carlo method

Abstract

The hidden Markov random field (HMRF) model has been widely used in image segmentation, as it provides a spatially constrained clustering scheme on two sets of random variables. However, in many HMRF-based segmentation approaches, both the latent class labels and statistical parameters have been estimated by deterministic techniques, which usually lead to local convergence and less accurate segmentation. In this paper, we incorporate the immune inspired clonal selection algorithm (CSA) and Markov chain Monte Carlo (MCMC) method into HMRF model estimation, and thus propose the HMRF–CSA algorithm for brain MR image segmentation. Our algorithm employs a three-step iterative process that consists of MCMC-based class labels estimation, bias field correction and CSA-based statistical parameter estimation. Since both the MCMC and CSA are global optimization techniques, the proposed algorithm has the potential to overcome the drawback of traditional HMRF-based segmentation approaches. We compared our algorithm to the state-of-the-art GA–EM algorithm, deformable cosegmentation algorithm, the segmentation routines in the widely-used statistical parametric mapping (SPM) software package and the FMRIB software library (FSL) on both simulated and clinical brain MR images. Our results show that the proposed HMRF–CSA algorithm is robust to image artifacts and can differentiate major brain structures more accurately than other three algorithms.