Automated medical image segmentation.
| dc.contributor.advisor | Schubert, Keith Evan. | |
| dc.creator | Nguyen, Hanh Hong, 1991- | |
| dc.date.accessioned | 2018-01-25T14:31:09Z | |
| dc.date.available | 2018-01-25T14:31:09Z | |
| dc.date.created | 2017-12 | |
| dc.date.issued | 2017-12-04 | |
| dc.date.submitted | December 2017 | |
| dc.date.updated | 2018-01-25T14:31:09Z | |
| dc.description.abstract | Computed Tomography (CT) is one of the most common medical diagnostic imaging techniques. Since the first clinical CT scanner was installed in the 1970s, there are about 30,000 CT scanners installed worldwide. Despite of the vast number of scanners and the improvement in image quality, the demand of accuracy in differentiating different region and type of tissue in the treatment area of the patient body is still remains. Some materials are easy to identify while some are not due to their thin shape and/or the limited resolution of the scans. This thesis addresses the problem by investigating several image segmentation techniques to achieve fast performance and better quality tissue assignment. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/2104/10195 | |
| dc.language.iso | en | |
| dc.rights.accessrights | Worldwide access. | |
| dc.subject | Medical image. Auto segmentation. | |
| dc.title | Automated medical image segmentation. | |
| dc.type | Thesis | |
| dc.type.material | text | |
| thesis.degree.department | Baylor University. Dept. of Electrical & Computer Engineering. | |
| thesis.degree.grantor | Baylor University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | M.S.B.M.E. |
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