Population-wise consistent segmentation of diffusion weighted magnetic resonance images
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2009
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| Online Access: | http://hdl.handle.net/1721.1/44721 |
| _version_ | 1826205729509343232 |
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| author | Ziyan, Ulas |
| author2 | Carl-Fredrick Westin and W. Eric L. Grimson. |
| author_facet | Carl-Fredrick Westin and W. Eric L. Grimson. Ziyan, Ulas |
| author_sort | Ziyan, Ulas |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. |
| first_indexed | 2024-09-23T13:17:41Z |
| format | Thesis |
| id | mit-1721.1/44721 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:17:41Z |
| publishDate | 2009 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/447212019-04-11T08:54:08Z Population-wise consistent segmentation of diffusion weighted magnetic resonance images Ziyan, Ulas Carl-Fredrick Westin and W. Eric L. Grimson. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008. Includes bibliographical references (p. 161-167). In this thesis, we investigate unsupervised and semi-supervised methods to construct anatomical atlases and segment medical images. We propose an integrated registration and clustering algorithm to compute an anatomical atlas of fiber-bundles as well as deep gray matter structures from a population of diffusion tensor MR images (DT-MRI). We refer to this algorithm as "Consistency Clustering" since the outputs of the algorithm include population-wise consistent segmentations and correspondence between the subjects. The consistency is ensured through using a single anatomical model for the whole population, which is similar to the atlases used by experts for manual labeling. We experiment with both parametric and non-parametric models for the gray matter and white matter segmentation problems, each model resulting in a different kind of atlas. Consistent population-wise segmentations require development of several integrated algorithms for clustering, registration, atlas-building and outlier rejection. In this thesis we develop, implement and evaluate these tools individually and together as a population-wise segmentation tool. Together, Consistency Clustering enables automatic atlas construction in DT-MRI for a population, either normal or affected by a neural disorder. Consistency Clustering also provides the user the choice to include prior knowledge through a few labeled subjects (semi-supervised) or compute an anatomical atlas in a completely data driven manner (unsupervised). Furthermore, resulting anatomical models are compact representations of populations and can be used for population-wise morphometry. We implement and evaluate these methods using in vivo DT-MRI datasets. We investigate the benefits of population-wise segmentation as opposed to individually segmenting subjects, as well as effects of noise and initialization on the segmentations. by Ulas Ziyan. Ph.D. 2009-03-16T19:34:20Z 2009-03-16T19:34:20Z 2008 2008 Thesis http://hdl.handle.net/1721.1/44721 298116023 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 167 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Ziyan, Ulas Population-wise consistent segmentation of diffusion weighted magnetic resonance images |
| title | Population-wise consistent segmentation of diffusion weighted magnetic resonance images |
| title_full | Population-wise consistent segmentation of diffusion weighted magnetic resonance images |
| title_fullStr | Population-wise consistent segmentation of diffusion weighted magnetic resonance images |
| title_full_unstemmed | Population-wise consistent segmentation of diffusion weighted magnetic resonance images |
| title_short | Population-wise consistent segmentation of diffusion weighted magnetic resonance images |
| title_sort | population wise consistent segmentation of diffusion weighted magnetic resonance images |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/44721 |
| work_keys_str_mv | AT ziyanulas populationwiseconsistentsegmentationofdiffusionweightedmagneticresonanceimages |