Segmenting internal structures in 3D MR images of the brain by Markovian relaxation on a watershed based adjacency graph

The authors present a fast stochastic method aiming at segmenting cerebral internal structures in 3D magnetic resonance images. An original method introducing context permits the authors to obtain reliable radiometric characteristics even for hardly discriminable brain structures. Segmentation is formulated as the labeling of a region adjacency graph. The graph is constructed by an extension to 3D of the watershed algorithm and the labeling is performed using a Markovian relaxation process. This leads to consistent results with a very low computational burden.