An Investigation of Mixture Segmentation of Multispectral MR Brain Images for Multiple Sclerosis

摘要

We present a new mixture model-based tissue classification of multispectral (T_1-, T_2-, and P_D-weighted) magnetic resonance (MR) brain images. Unlike the conventional hard classification with a unique label for each voxel, our method models a mixture to estimate the partial volumes (PV) of multiple tissue types within a voxel. A new Markov random field (MRF) model is proposed to reflect the spatial information of tissue mixtures. A mixture classification algorithm is performed by the maximum a posterior (MAP) criterion, where the expectation maximization (EM) algorithm is utilized to estimate model parameters. The presented method is evaluated by clinical MR image datasets.