title: Near-tubular fiber bundle segmentation for diffusion weighted imaging: segmentation through frame reorientation.
abstract: This paper proposes a methodology to segment near-tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. Segmentation is achieved through simple global statistical modeling of diffusion orientation. Utilizing a modification of a recent segmentation approach by Bresson et al. allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares favorably with segmentation by full-brain streamline tractography.
authors.affiliation: University of North Carolina at Chapel Hill, Department of Computer Science, Chapel Hill, NC, USA. mn@cs.unc.edu
authors.forename: Marc
authors.initials: M
authors.lastname: Niethammer
authors.forename: Christopher
authors.initials: C
authors.lastname: Zach
authors.forename: John
authors.initials: J
authors.lastname: Melonakos
authors.forename: Allen
authors.initials: A
authors.lastname: Tannenbaum
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology*; D038524:Diffusion Magnetic Resonance Imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015233:Models, Statistical; D009434:Neural Pathways / Q000033:anatomy & histology