Surface Segmentation and Topology

Jerry Prince
Johns Hopkins University
Department of Electrical and Computer Engineering

Segmentation and representation of the human cerebral cortex from magnetic
resonance (MR) images plays an important role in neuroscience and medicine. A
successful segmentation method must be robust to various imaging artifacts and
produce anatomically meaningful and consistent cortical representations. A method for
the automatic reconstruction of the inner, central, and outer surfaces of the cerebral
cortex from T1-weighted MR brain images is presented. The method combines a fuzzy
tissue classification method, an efficient topology correction algorithm, and a topologypreserving
geometric deformable surface model. The algorithm is fast and numerically
stable, and yields accurate brain surface reconstructions that are guaranteed to be
topologically correct and free from self-intersections. Methods for flattening, segmenting
sulci, and registering cortical surfaces will also be described. Results demonstrating the
application of this overall approach to a study of cortical thickness changes in aging are

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