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 play 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 approach combines fuzzy tissue classification, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model. The algorithm is fast and numerically stable, producing accurate brain surface reconstructions that are guaranteed to be topologically correct and free of self-intersections.
Methods for cortical flattening, sulcal segmentation, and surface registration are also described. Results demonstrating the application of this framework to the study of cortical thickness changes in aging are presented.
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