Automated model-based tissue classification of MR images of the brain

We describe a fully automated method for model-based tissue classification of magnetic resonance (MR) images of the brain, The method interleaves clas sification with estimation of the model parameters, improving the classific ation at each iteration. The algorithm is able to segment single- and multi spectral MR images, corrects for MR signal inhomogeneities, and incorporate s contextual information by means of Markov random Fields (MRF's). A digita l brain atlas containing prior expectations about the spatial location of t issue classes is used to initialize the algorithm. This makes the method fu lly automated and therefore it provides objective and reproducible segmenta tions, We have validated the technique on simulated as well as on real MR i mages of the brain.