In the context of brain tissue classification within magnetic resonance (MR) volume sequences segmentation of particular anatomical entities states a challenging problem for fully automated approaches: the distinction of deep gray matter structures, like the caudate nuclei, and cortical gray matter based on observed intensities only, is virtually impossible. Prior knowledge about the anatomical composition of the human brain has to be integrated to guide the segmentation process. Furthermore, segmentation methods need to be robust with regard to the characteristic artifacts of the MR imaging modality: Rician noise, partial volume effects, and intensity inhomogeneities. This project deals with the fast robust fully automatic segmentation of deep gray matter structures in 3-D MRI.