title: CRUISE: cortical reconstruction using implicit surface evolution.
abstract: 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 method combines a fuzzy tissue classification method, an efficient topology correction algorithm, and a topology-preserving geometric deformable surface model (TGDM). 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. Validation results on real MR data are presented to demonstrate the performance of the method.
authors.affiliation: Department of Electrical and Computer Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, MD 21218 USA.
authors.forename: Xiao
authors.initials: X
authors.lastname: Han
authors.forename: Dzung L
authors.initials: DL
authors.lastname: Pham
authors.forename: Duygu
authors.initials: D
authors.lastname: Tosun
authors.forename: Maryam E
authors.initials: ME
authors.lastname: Rettmann
authors.forename: Chenyang
authors.initials: C
authors.lastname: Xu
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000502:physiology*; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods* / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D015203:Reproducibility of Results