Difference between revisions of "Projects:DWIReorientation"
From NAMIC Wiki
| (3 intermediate revisions by 2 users not shown) | |||
| Line 1: | Line 1: | ||
| − | Back to | + | Back to [[Algorithm:Stony Brook|Stony Brook University Algorithms]] |
__NOTOC__ | __NOTOC__ | ||
= Re-Orientation Approach for Segmentation of DW-MRI = | = Re-Orientation Approach for Segmentation of DW-MRI = | ||
| Line 14: | Line 14: | ||
= Publications = | = Publications = | ||
''In Print'' | ''In Print'' | ||
| − | * [http://www.na-mic.org/publications/pages/display?search=Projects%3ADWIReorientation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database] | + | * [http://www.na-mic.org/publications/pages/display?search=Projects%3ADWIReorientation&submit=Search&words=all&title=checked&keywords=checked&authors=checked&abstract=checked&sponsors=checked&searchbytag=checked| NA-MIC Publications Database on Re-Orientation Approach for Segmentation of DW-MRI] |
[[Category: MRI]] | [[Category: MRI]] | ||
Latest revision as of 01:00, 16 November 2013
Home < Projects:DWIReorientationBack to Stony Brook University Algorithms
Re-Orientation Approach for Segmentation of DW-MRI
Description
This work 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 which allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares very favorably with segmentation by full-brain streamline tractography. See Figure 4.
Key Investigators
Georgia Tech: Allen Tannenbaum, Marc Niethammer, John Melonakos
Publications
In Print
