Model driven segmentation and the detection of bone fractures

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dc.contributor.advisorMarais, Patricken_ZA
dc.contributor.authorMarte, Otto-Carlen_ZA
dc.date.accessioned2014-08-13T19:31:23Z
dc.date.available2014-08-13T19:31:23Z
dc.date.issued2004en_ZA
dc.descriptionBibliography: leaves 83-90.en_ZA
dc.description.abstractThe introduction of lower dosage image acquisition devices and the increase in computational power means that there is an increased focus on producing diagnostic aids for the medical trauma environment. The focus of this research is to explore whether geometric criteria can be used to detect bone fractures from Computed Tomography data. Conventional image processing of CT data is aimed at the production of simple iso-surfaces for surgical planning or diagnosis - such methods are not suitable for the automated detection of fractures. Our hypothesis is that through a model-based technique a triangulated surface representing the bone can be speedily and accurately produced. And, that there is sufficient structural information present that by examining the geometric structure of this representation we can accurately detect bone fractures. In this dissertation we describe the algorithms and framework that we built to facilitate the detection of bone fractures and evaluate the validity of our approach.en_ZA
dc.identifier.apacitationMarte, O. (2004). <i>Model driven segmentation and the detection of bone fractures</i>. (Thesis). University of Cape Town ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/6414en_ZA
dc.identifier.chicagocitationMarte, Otto-Carl. <i>"Model driven segmentation and the detection of bone fractures."</i> Thesis., University of Cape Town ,Faculty of Science ,Department of Computer Science, 2004. http://hdl.handle.net/11427/6414en_ZA
dc.identifier.citationMarte, O. 2004. Model driven segmentation and the detection of bone fractures. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Marte, Otto-Carl AB - The introduction of lower dosage image acquisition devices and the increase in computational power means that there is an increased focus on producing diagnostic aids for the medical trauma environment. The focus of this research is to explore whether geometric criteria can be used to detect bone fractures from Computed Tomography data. Conventional image processing of CT data is aimed at the production of simple iso-surfaces for surgical planning or diagnosis - such methods are not suitable for the automated detection of fractures. Our hypothesis is that through a model-based technique a triangulated surface representing the bone can be speedily and accurately produced. And, that there is sufficient structural information present that by examining the geometric structure of this representation we can accurately detect bone fractures. In this dissertation we describe the algorithms and framework that we built to facilitate the detection of bone fractures and evaluate the validity of our approach. DA - 2004 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2004 T1 - Model driven segmentation and the detection of bone fractures TI - Model driven segmentation and the detection of bone fractures UR - http://hdl.handle.net/11427/6414 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/6414
dc.identifier.vancouvercitationMarte O. Model driven segmentation and the detection of bone fractures. [Thesis]. University of Cape Town ,Faculty of Science ,Department of Computer Science, 2004 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/6414en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentDepartment of Computer Scienceen_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherComputer Scienceen_ZA
dc.titleModel driven segmentation and the detection of bone fracturesen_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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