Geodesic tractography segmentation for directional medical image analysis
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 probl...
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ndltd-GATECH-oai-smartech.gatech.edu-1853-281392014-01-07T03:37:15ZGeodesic tractography segmentation for directional medical image analysisMelonakos, JohnMedical image analysisMRITractographyImage processingDiagnostic imagingGeodesics (Mathematics)Geodesic flowsGeodesic 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.Acknowledgements page removed per author's request, 01/06/2014.Georgia Institute of Technology2009-06-08T19:02:34Z2009-06-08T19:02:34Z2008-12-17Dissertationhttp://hdl.handle.net/1853/28139 |
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Medical image analysis MRI Tractography Image processing Diagnostic imaging Geodesics (Mathematics) Geodesic flows |
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Medical image analysis MRI Tractography Image processing Diagnostic imaging Geodesics (Mathematics) Geodesic flows Melonakos, John Geodesic tractography segmentation for directional medical image analysis |
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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. === Acknowledgements page removed per author's request, 01/06/2014. |
author |
Melonakos, John |
author_facet |
Melonakos, John |
author_sort |
Melonakos, John |
title |
Geodesic tractography segmentation for directional medical image analysis |
title_short |
Geodesic tractography segmentation for directional medical image analysis |
title_full |
Geodesic tractography segmentation for directional medical image analysis |
title_fullStr |
Geodesic tractography segmentation for directional medical image analysis |
title_full_unstemmed |
Geodesic tractography segmentation for directional medical image analysis |
title_sort |
geodesic tractography segmentation for directional medical image analysis |
publisher |
Georgia Institute of Technology |
publishDate |
2009 |
url |
http://hdl.handle.net/1853/28139 |
work_keys_str_mv |
AT melonakosjohn geodesictractographysegmentationfordirectionalmedicalimageanalysis |
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1716622963293290496 |