Segmentation of brain x-ray CT images using seeded region growing

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dc.contributor.advisorDe Jager, Gerharden_ZA
dc.contributor.authorBub, Alan Marken_ZA
dc.date.accessioned2016-03-04T16:32:21Z
dc.date.available2016-03-04T16:32:21Z
dc.date.issued1996en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractThree 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.en_ZA
dc.identifier.apacitationBub, A. M. (1996). <i>Segmentation of brain x-ray CT images using seeded region growing</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/17436en_ZA
dc.identifier.chicagocitationBub, Alan Mark. <i>"Segmentation of brain x-ray CT images using seeded region growing."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1996. http://hdl.handle.net/11427/17436en_ZA
dc.identifier.citationBub, A. 1996. Segmentation of brain x-ray CT images using seeded region growing. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Bub, Alan Mark AB - 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. DA - 1996 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 1996 T1 - Segmentation of brain x-ray CT images using seeded region growing TI - Segmentation of brain x-ray CT images using seeded region growing UR - http://hdl.handle.net/11427/17436 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/17436
dc.identifier.vancouvercitationBub AM. Segmentation of brain x-ray CT images using seeded region growing. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 1996 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/17436en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherComputerized Tomography (CT) imagesen_ZA
dc.titleSegmentation of brain x-ray CT images using seeded region growingen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMScen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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