Brain voxel classification in magnetic resonance images using niche differential evolution based Bayesian inference of variational mixture of Gaussians

Classification of brain voxels into gray matter, white matter, and cerebrospinal fluid (CSF) using magnetic resonance imaging (MRI) is pivotal for quantitative brain analyses. In spite of its computational effectiveness, the most commonly used statistical classification models are less capable of handling the intensity non-uniformity (INU) and partial volume effect (PVE), and hence may produce less accurate results. In this paper, we propose a novel approach, namely the VMG-NDE algorithm, to improve brain voxel classification in MRI images by considering all effects simultaneously. There are four planks in this algorithm, including (1) using variational mixture of Gaussians (VMG) model to characterize the variation of voxel values caused by PVE, (2) training a cohort of local VMG models on small data volumes extracted from the image to reduce the impact of INU, (3) employing the niche differential evolution (NDE) to infer each local VMG model, aiming to avoid falling into local optima, and (4) constructing a probabilistic brain atlas for each study and using it to incorporate the anatomy prior into the classification process. After training local VMG models, we classify each brain voxel using a linear combination of the predictions generated by all those models. This algorithm has been evaluated against the variational expectation-maximization based and genetic algorithm based segmentation algorithms and the segmentation routines in the widely used statistical parametric mapping (SPM) package, expectation-maximization segmentation (EMS) package and FSL package on both synthetic and clinical T1-weighted brain MRI studies. Our results suggest that the proposed algorithm can differentiate major brain tissue types more effectively and produce improved brain voxel classification accuracy.