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
presented.
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