An efficient brain magnetic resonance image segmentation method
Pan Lin, Yong Yang, Chongxun Zheng · 2005
An efficient statistical model tissue classification algorithm is proposed for the segmentation of brain magnetic resonance images. Due to the partial volume effects, many voxels may be composed of a multiple tissue types. To solve this problem, we present an efficient method for brain magnetic resonance images classification. The method uses the Bayesian contextual classifier based on Markov random field models. In the algorithm, each mixture voxels in the MR image is labeled using the maximum a posteriori classifier. A spatial prior is defined based on homogeneous regions while taking into different tissue mixtures. The efficacy of the proposed algorithm is demonstrated by extensive experiments using phantom data.