Segmentation of nerve bundles and ganglia in spine MRI using particle filters
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2012
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| Online Access: | http://hdl.handle.net/1721.1/75654 |
| _version_ | 1811093587708870656 |
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| author | Dalca, Adrian Vasile |
| author2 | Polina Golland. |
| author_facet | Polina Golland. Dalca, Adrian Vasile |
| author_sort | Dalca, Adrian Vasile |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. |
| first_indexed | 2024-09-23T15:47:29Z |
| format | Thesis |
| id | mit-1721.1/75654 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T15:47:29Z |
| publishDate | 2012 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/756542019-04-12T13:57:38Z Segmentation of nerve bundles and ganglia in spine MRI using particle filters Dalca, Adrian Vasile Polina Golland. 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 (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. Cataloged from PDF version of thesis. Includes bibliographical references (p. 41-44). Automatic segmentation of spinal nerve bundles originating within the dural sac and exiting the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this thesis, we present an automatic tracking method for segmentation of nerve bundles based on particle filters. We develop a novel approach to flexible particle representation of tubular structures based on Bezier splines. We construct an appropriate dynamics to reflect the continuity and smoothness properties of real nerve bundles. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We evaluate the results by comparing them to expert manual segmentation, and we demonstrate accurate and fast nerve tracking. by Adrian Vasile Dalca. S.M. 2012-12-13T18:49:35Z 2012-12-13T18:49:35Z 2012 2012 Thesis http://hdl.handle.net/1721.1/75654 818357131 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 44 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Dalca, Adrian Vasile Segmentation of nerve bundles and ganglia in spine MRI using particle filters |
| title | Segmentation of nerve bundles and ganglia in spine MRI using particle filters |
| title_full | Segmentation of nerve bundles and ganglia in spine MRI using particle filters |
| title_fullStr | Segmentation of nerve bundles and ganglia in spine MRI using particle filters |
| title_full_unstemmed | Segmentation of nerve bundles and ganglia in spine MRI using particle filters |
| title_short | Segmentation of nerve bundles and ganglia in spine MRI using particle filters |
| title_sort | segmentation of nerve bundles and ganglia in spine mri using particle filters |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/75654 |
| work_keys_str_mv | AT dalcaadrianvasile segmentationofnervebundlesandgangliainspinemriusingparticlefilters |