title: Automatic segmentation of human brain sulci.
abstract: The neocortical surface has a rich and complex structure comprised of folds (gyri) and fissures (sulci). Sulci are important macroscopic landmarks for orientation on the cortex. A precise segmentation and labeling of sulci is helpful in human brain mapping studies relating brain anatomy and function. Due to their structural complexity and inter-subject variability, this is considered as a non-trivial task. An automatic algorithm is proposed to accurately segment neocortical sulci: vertices of a white/gray matter interface mesh are classified under a Bayesian framework as belonging to gyral and sulcal compartments using information about their geodesic depth and local curvature. Then, vertices are collected into sulcal regions by a watershed-like growing method. Experimental results demonstrate that the method is accurate and robust.
authors.affiliation: Signal and Image Processing Laboratory, Department of Biomedical Engineering, University of California, Irvine, CA 92697-2755, United States.
authors.forename: Faguo
authors.initials: F
authors.lastname: Yang
authors.forename: Frithjof
authors.initials: F
authors.lastname: Kruggel
mesh_terms: D000328:Adult; D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D016208:Databases, Factual; D005260:Female; D006801:Humans; D008297:Male