Prior information for brain parcellation
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
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| Formato: | Tese |
| Idioma: | eng |
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Massachusetts Institute of Technology
2008
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| Acesso em linha: | http://dspace.mit.edu/handle/1721.1/33925 http://hdl.handle.net/1721.1/33925 |
| _version_ | 1826205121462140928 |
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| author | Pohl, Kilian Maria |
| author2 | W. Eric L. Grimson and Ron Kikinis. |
| author_facet | W. Eric L. Grimson and Ron Kikinis. Pohl, Kilian Maria |
| author_sort | Pohl, Kilian Maria |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005. |
| first_indexed | 2024-09-23T13:07:24Z |
| format | Thesis |
| id | mit-1721.1/33925 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:07:24Z |
| publishDate | 2008 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/339252019-04-12T14:35:51Z Prior information for brain parcellation Pohl, Kilian Maria W. Eric L. Grimson and Ron Kikinis. 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, 2005. Includes bibliographical references (p. 171-184). To better understand brain disease, many neuroscientists study anatomical differences between normal and diseased subjects. Frequently, they analyze medical images to locate brain structures influenced by disease. Many of these structures have weakly visible boundaries so that standard image analysis algorithms perform poorly. Instead, neuroscientists rely on manual procedures, which are time consuming and increase risks related to inter- and intra-observer reliability [53]. In order to automate this task, we develop an algorithm that robustly segments brain structures. We model the segmentation problem in a Bayesian framework, which is applicable to a variety of problems. This framework employs anatomical prior information in order to simplify the detection process. In this thesis, we experiment with different types of prior information such as spatial priors, shape models, and trees describing hierarchical anatomical relationships. We pose a maximum a posteriori probability estimation problem to find the optimal solution within our framework. From the estimation problem we derive an instance of the Expectation Maximization algorithm, which uses an initial imperfect estimate to converge to a good approximation. (cont.) The resulting implementation is tested on a variety of studies, ranging from the segmentation of the brain into the three major brain tissue classes, to the parcellation of anatomical structures with weakly visible boundaries such as the thalamus or superior temporal gyrus. In general, our new method performs significantly better than other :standard automatic segmentation techniques. The improvement is due primarily to the seamless integration of medical image artifact correction, alignment of the prior information to the subject, detection of the shape of anatomical structures, and representation of the anatomical relationships in a hierarchical tree. by Kilian Maria Pohl. Ph.D. 2008-04-24T08:54:15Z 2008-04-24T08:54:15Z 2005 2005 Thesis http://dspace.mit.edu/handle/1721.1/33925 http://hdl.handle.net/1721.1/33925 67296469 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/1721.1/33925 http://dspace.mit.edu/handle/1721.1/7582 184 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Pohl, Kilian Maria Prior information for brain parcellation |
| title | Prior information for brain parcellation |
| title_full | Prior information for brain parcellation |
| title_fullStr | Prior information for brain parcellation |
| title_full_unstemmed | Prior information for brain parcellation |
| title_short | Prior information for brain parcellation |
| title_sort | prior information for brain parcellation |
| topic | Electrical Engineering and Computer Science. |
| url | http://dspace.mit.edu/handle/1721.1/33925 http://hdl.handle.net/1721.1/33925 |
| work_keys_str_mv | AT pohlkilianmaria priorinformationforbrainparcellation |