Tubular Fiber Bundles Segmentation for Diffusion Weighted Images

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/29317

Title: Tubular Fiber Bundles Segmentation for Diffusion Weighted Images
Author: Niethammer, Marc ; Zach, Christopher ; Melonakos, John ; Tannenbaum, Allen R.
Abstract: This paper proposes a methodology to segment 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. [19] 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.
Description: Presented at CDMRI’08, MICCAI Workshop on Computational Diffusion MRI, September 10th, 2008, Kimmel Center, New York City, USA.
Type: Proceedings
URI: http://hdl.handle.net/1853/29317
Citation: Marc Niethammer, Christopher Zach, John Melonakos, and Allen Tannenbaum, "Tubular Fiber Bundles Segmentation for Diffusion Weighted Images," MICCAI Workshop on Computational Diffusion MRI, 2008, 265-276
Date: 2008-09-10
Contributor: Georgia Institute of Technology. Dept. of Biomedical Engineering
Emory University. Dept. of Biomedical Engineering
Georgia Institute of Technology. School of Electrical and Computer Engineering
University of North Carolina at Chapel Hill. Dept. of Computer Science
University of North Carolina at Chapel Hill. Biomedical Research Imaging Center
Publisher: Georgia Institute of Technology
Subject: Biomedical MRI
Brain MRI scans
Image segmentation
Statistical analysis
Diffusion weighted magnetic resonance images
DW-MRI
Tubular fiber bundles

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