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= Re-Orientation Approach for Segmentation of DW-MRI =
 
= Re-Orientation Approach for Segmentation of DW-MRI =
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Georgia Tech: Allen Tannenbaum, Marc Niethammer, John Melonakos
 
Georgia Tech: Allen Tannenbaum, Marc Niethammer, John Melonakos
 
  
 
= 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]
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* [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]]
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Latest revision as of 01:00, 16 November 2013

Home < Projects:DWIReorientation
Back 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