{"id":{"repo_id":"cape-town","oai_identifier":"oai:open.uct.ac.za:11427/17436"},"canonical_url":"https://search.dev.ndltd.org/etd/cape-town/oai:open.uct.ac.za:11427/17436","repository":{"repo_id":"cape-town","name":"University of Cape Town","base_url":"https://open.uct.ac.za/oai/request"},"display":{"title":"Segmentation of brain x-ray CT images using seeded region growing","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Bub, Alan Mark"],"institution":"Department of Electrical Engineering","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["De Jager, Gerhard"],"committee_chairs":[],"committee_members":[],"year":1996,"date_issued":"1996","date_published":"1996","updated_at":"2026-07-22T22:22:58Z","subjects":[],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11427/17436","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["De Jager, Gerhard"]},{"key":"dc:creator","label":"Author","values":["Bub, Alan Mark"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-03-04T16:32:21Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-03-04T16:32:21Z"]},{"key":"dc:date.issued","label":"Date","values":["1996"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Department of Electrical Engineering"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cape Town"]},{"key":"dc:type","label":"Dc Type","values":["Master Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Masters"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["MSc"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11427/17436"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Includes bibliographical references."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Segmentation of brain x-ray CT images using seeded region growing"]}]}],"canonical_facts":{"dc:contributor.advisor":["De Jager, Gerhard"],"dc:creator":["Bub, Alan Mark"],"dc:date.accessioned":["2016-03-04T16:32:21Z"],"dc:date.available":["2016-03-04T16:32:21Z"],"dc:date.issued":["1996"],"dc:description":["Includes bibliographical references."],"dc:description.abstract":["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."],"dc:identifier.uri":["http://hdl.handle.net/11427/17436"],"dc:language.iso":["eng"],"dc:publisher.department":["Department of Electrical Engineering"],"dc:publisher.institution":["University of Cape Town"],"dc:title":["Segmentation of brain x-ray CT images using seeded region growing"],"dc:type":["Master Thesis"],"dc:type.qualificationlevel":["Masters"],"dc:type.qualificationname":["MSc"]},"updated_at":"2026-07-22T22:22:58Z"}