Brain tissue classification based on a mixel model and Markov random field models

International audience; This paper presents a fully-automatic 3D classification of brain tissues for Magnetic Resonance (MR) images. A MR image volume may be composed of a mixture of several tissue types due to partial volume effects. Therefore, we consider that in a brain dataset there are not only the three principal brain tissues : gray matter (GM), white matter (WM) and cerebral spinal fluid (CSF), called pure classes, but also mixtures, called mixclasses. The statistical midel of the mixtures is proposed and studied by means of simulations. The D’Agostino-Pearson normality test is used to calculate the risk alpha of the approximation. Both steps (segmentation and reclassification) use Markov Random Field (MRF) models. The multifractal dimension, describing the topology of the brain, is added to the MRF’s to improve the discrimination of the mixclasses.The algorithm is evaluated using both simulated images and real MR images with different T1-weighted acquisition sequences.