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Segmentation of brain x-ray CT images using seeded region growing
Includes bibliographical references.
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| Main Author: | |
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
| Published: |
Department of Electrical Engineering
2016
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| _version_ | 1867613221112774656 |
|---|---|
| access_status_str | Open Access |
| author | Bub, Alan Mark |
| author2 | De Jager, Gerhard |
| author_browse | Bub, Alan Mark De Jager, Gerhard |
| author_facet | De Jager, Gerhard Bub, Alan Mark |
| author_sort | Bub, Alan Mark |
| collection | Thesis |
| description | Includes bibliographical references. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/17436 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:41.376Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | Department of Electrical Engineering |
| publisherStr | Department of Electrical Engineering |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/17436 Segmentation of brain x-ray CT images using seeded region growing Bub, Alan Mark De Jager, Gerhard Computerized Tomography (CT) images Includes bibliographical references. Three problems are addressed in this dissertation. They are intracranial volume extraction, noise suppression and automated segmentation of X-Ray Computerized Tomography (CT) images. The segmentation scheme is based on a Seeded Region Growing algorithm. The intracranial volume extraction is based on image symmetry and the noise suppression filter is based on the Gaussian nature of the tissue distribution. Both are essential in achieving good segmentation results. Simulated phantoms and real medical images were used in testing and development of the algorithms. The testing was done over a wide range of noise values, object sizes and mean object grey levels. All the methods were first implemented in two- and then three-dimensions. The 3-D implementation also included an investigation into volume formation and the advantages of 3-D processing. The results of the intracranial extraction showed that 9% of the data in the relevant grey level range consisted of unwanted scalp (The scalp is spatially not part of the intracranial volume, but has the same grey level values). This justified the extraction the intracranial volume for further processing. For phantom objects greater than 741.51mm³ (voxel resolution 0.48mm x 0.48mm x 2mm) and having a mean grey level distance of 10 from any other object, a maximum segmentation volume error of 15% was achieved. 2016-03-04T16:32:21Z 2016-03-04T16:32:21Z 1996 Master Thesis Masters MSc http://hdl.handle.net/11427/17436 eng application/pdf Department of Electrical Engineering Faculty of Engineering and the Built Environment University of Cape Town |
| spellingShingle | Computerized Tomography (CT) images Bub, Alan Mark Segmentation of brain x-ray CT images using seeded region growing |
| thesis_degree_str | Master's |
| title | Segmentation of brain x-ray CT images using seeded region growing |
| title_full | Segmentation of brain x-ray CT images using seeded region growing |
| title_fullStr | Segmentation of brain x-ray CT images using seeded region growing |
| title_full_unstemmed | Segmentation of brain x-ray CT images using seeded region growing |
| title_short | Segmentation of brain x-ray CT images using seeded region growing |
| title_sort | segmentation of brain x ray ct images using seeded region growing |
| topic | Computerized Tomography (CT) images |
| url | http://hdl.handle.net/11427/17436 |
| work_keys_str_mv | AT bubalanmark segmentationofbrainxrayctimagesusingseededregiongrowing |