dc.contributor.author | Melonakos, John | en_US |
dc.date.accessioned | 2009-06-08T19:02:34Z | |
dc.date.available | 2009-06-08T19:02:34Z | |
dc.date.issued | 2008-12-17 | en_US |
dc.identifier.uri | http://hdl.handle.net/1853/28139 | |
dc.description | Acknowledgements page removed per author's request, 01/06/2014. | en_US |
dc.description.abstract | Geodesic Tractography Segmentation is the two component approach presented in this thesis for the analysis of imagery in oriented domains, with emphasis on the application to diffusion-weighted magnetic resonance imagery (DW-MRI). The computeraided analysis of DW-MRI data presents a new set of problems and opportunities for the application of mathematical and computer vision techniques. The goal is to develop a set of tools that enable clinicians to better understand DW-MRI data and ultimately shed new light on biological processes.
This thesis presents a few techniques and tools which may be used to automatically find and segment major neural fiber bundles from DW-MRI data. For each technique, we provide a brief overview of the advantages and limitations of our approach relative to other available approaches. | en_US |
dc.publisher | Georgia Institute of Technology | en_US |
dc.subject | Medical image analysis | en_US |
dc.subject | MRI | en_US |
dc.subject | Tractography | en_US |
dc.subject | Image processing | en_US |
dc.subject.lcsh | Diagnostic imaging | |
dc.subject.lcsh | Geodesics (Mathematics) | |
dc.subject.lcsh | Geodesic flows | |
dc.title | Geodesic tractography segmentation for directional medical image analysis | en_US |
dc.type | Dissertation | en_US |
dc.description.degree | Ph.D. | en_US |
dc.contributor.department | Electrical and Computer Engineering | en_US |
dc.description.advisor | Committee Chair: Tannenbaum, Allen; Committee Member: Barnes, Christopher F.; Committee Member: Niethammer, Marc; Committee Member: Shamma, Jeff; Committee Member: Vela, Patricio | en_US |