Automatic detection and segmentation of brain lesions from 3D MR and CT images

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dc.contributor.advisorNicolls, Freden_ZA
dc.contributor.advisorDe Jager, Gerharden_ZA
dc.contributor.advisorMuller, Nen_ZA
dc.contributor.authorMokhomo, Moliseen_ZA
dc.date.accessioned2014-11-05T03:35:37Z
dc.date.available2014-11-05T03:35:37Z
dc.date.issued2014en_ZA
dc.descriptionIncludes bibliographical references.en_ZA
dc.description.abstractThe detection and segmentation of brain pathologies in medical images is a vital step which helps radiologists to diagnose a variety of brain abnormalities and set up a suitable treatment. A number of institutes such as iThemba LABS still rely on a manual identification of abnormalities. A manual identification is labour intensive and tedious due to the large amount of medical data to be processed and the presence of small lesions. This thesis discusses the possible methods that can be used to address the problem of brain abnormality segmentation in MR and CT images. The methods are general enough to segment different types of abnormalities. The first method is based on the symmetry of the brain while the second method is based on a brain atlas. The symmetry-based method assumes that healthy brain tissues are symmetrical in nature while abnormal tissues are asymmetric with respect to the symmetry plane dividing the brain into similar hemispheres. The three major steps involved in this approach are the symmetry detection, tilt correction and asymmetry quantification. The method used to determine the brain symmetry automatically is discussed and its accuracy has been validated against the ground-truth using mean angular error (MAE) and distance error (DE). Two asymmetric quantification methods are studied and validated on real and simulated patient’s T1- and T2-weighted MR images with low and highgrade gliomas using true positive volume fraction (TPVF), false positive volume fraction (FPVF) and false negative volume fraction (FNVF). The atlas-based method is also presented and relies on the assumption that abnormal brain tissues appear with intensity values different from those of the surrounding healthy tissues. To detect and segment brain lesions the test image is aligned onto the atlas space and voxel by voxel analysis is performed between the atlas and the registered image. This methods is also evaluated on the simulated T1-weighted patient dataset with simulated low and high grade gliomas. The atlas, containing prior knowledge of normal brain tissues, is built from a set of healthy subjects.en_ZA
dc.identifier.apacitationMokhomo, M. (2014). <i>Automatic detection and segmentation of brain lesions from 3D MR and CT images</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/9089en_ZA
dc.identifier.chicagocitationMokhomo, Molise. <i>"Automatic detection and segmentation of brain lesions from 3D MR and CT images."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2014. http://hdl.handle.net/11427/9089en_ZA
dc.identifier.citationMokhomo, M. 2014. Automatic detection and segmentation of brain lesions from 3D MR and CT images. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Mokhomo, Molise AB - The detection and segmentation of brain pathologies in medical images is a vital step which helps radiologists to diagnose a variety of brain abnormalities and set up a suitable treatment. A number of institutes such as iThemba LABS still rely on a manual identification of abnormalities. A manual identification is labour intensive and tedious due to the large amount of medical data to be processed and the presence of small lesions. This thesis discusses the possible methods that can be used to address the problem of brain abnormality segmentation in MR and CT images. The methods are general enough to segment different types of abnormalities. The first method is based on the symmetry of the brain while the second method is based on a brain atlas. The symmetry-based method assumes that healthy brain tissues are symmetrical in nature while abnormal tissues are asymmetric with respect to the symmetry plane dividing the brain into similar hemispheres. The three major steps involved in this approach are the symmetry detection, tilt correction and asymmetry quantification. The method used to determine the brain symmetry automatically is discussed and its accuracy has been validated against the ground-truth using mean angular error (MAE) and distance error (DE). Two asymmetric quantification methods are studied and validated on real and simulated patient’s T1- and T2-weighted MR images with low and highgrade gliomas using true positive volume fraction (TPVF), false positive volume fraction (FPVF) and false negative volume fraction (FNVF). The atlas-based method is also presented and relies on the assumption that abnormal brain tissues appear with intensity values different from those of the surrounding healthy tissues. To detect and segment brain lesions the test image is aligned onto the atlas space and voxel by voxel analysis is performed between the atlas and the registered image. This methods is also evaluated on the simulated T1-weighted patient dataset with simulated low and high grade gliomas. The atlas, containing prior knowledge of normal brain tissues, is built from a set of healthy subjects. DA - 2014 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2014 T1 - Automatic detection and segmentation of brain lesions from 3D MR and CT images TI - Automatic detection and segmentation of brain lesions from 3D MR and CT images UR - http://hdl.handle.net/11427/9089 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/9089
dc.identifier.vancouvercitationMokhomo M. Automatic detection and segmentation of brain lesions from 3D MR and CT images. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2014 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/9089en_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.titleAutomatic detection and segmentation of brain lesions from 3D MR and CT imagesen_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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