Triplet Markov chain for 3D MRI brain segmentation using a probabilistic atlas

In this paper, we present a new Markovian scheme for MRI segmentation using a priori knowledge obtained from probability maps. Indeed we propose to use both triplet Markov chain and a brain atlas containing prior expectations about the spatial localization of the different tissue classes, to segment the brain in gray matter, white matter and cerebro-spinal fluid in an unsupervised way. Experimental results on real data are included to validate this approach. Comparison with other previously used techniques demonstrates the advantages (robustness, low computational complexity) of this new Markovian segmentation scheme using a probabilistic atlas.