title: Sheet-like white matter fiber tracts: representation, clustering, and quantitative analysis.
abstract: We introduce an automated and probabilistic method for subject-specific segmentation of sheet-like fiber tracts. In addition to clustering of trajectories into anatomically meaningful bundles, the method provides statistics of diffusion measures by establishing point correspondences on the estimated medial representation of each bundle. We also introduce a new approach for medial surface generation of sheet-like fiber bundles in order too initialize the proposed clustering algorithm. Applying the new method to a population study of brain aging on 24 subjects demonstrates the capabilities and strengths of the algorithm in identifying and visualizing spatial patterns of group differences.
authors: Neuroscience Program, SRI International, Menlo Park, CA 94025, USA.
authors: Mahnaz
authors: M
authors: Maddah
authors: James V
authors: JV
authors: Miller
authors: Edith V
authors: EV
authors: Sullivan
authors: Adolf
authors: A
authors: Pfefferbaum
authors: Torsten
authors: T
authors: Rohlfing
mesh_terms: D000328:Adult; D000368:Aged; D000375:Aging; D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016013:Likelihood Functions; D015233:Models, Statistical; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D011336:Probability