Maximum Pseudolikelihood Estimation for Mixture-Markov Random Field Segmentation of the Brain

Abstract

A popular method for segmentation of magnetic resonance images (MRI) of the brain is to use a mixture model of tissue intensities with an underlying Markov Random Field (MRF) to incorporate spatial dependence between neighbouring voxels. Most current available mixture-MRF-based implementations require the user to fix the values of the MRF parameters. There is no clear method of choosing these values. In this paper we propose the use of maximum pseudolikelihood (MPL) estimation of the MRF parameters, which has not previously been used in the context of MRI segmentation, and compare this to an existing least-squares (LS) estimator. We compare the performance of both estimators on real brain MRI, and also to fixing the MRF parameters. We found that the MPL estimator was better able to recover expert manual segmentations than the LS estimator, as measured by Dice coefficient. Likewise, estimation by either method was superior to fixing the MRF parameters.