| 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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| 2008_miccai_04.pdf | 362.8Kb |
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