The use of a priori model based information to guide segmentation and classification of MR images

This paper describes the rationale and importance of utilizing a priori, model based information to guide segmentation and classification in complex medical images represented by Magnetic Resonance Imaging (MRT). The incorporation of such a priori, model based information requires the development of a "top down" model driven system, rather than the more traditional "bottom up" data driven system. Two different examples which incorporate such model based a priori information are discussed: (1) The segmentation and classification of tissues involved in atherosclerosis; and (2) The segmentation and classification of brain tissue for neurosurgical applications.