Automatic detection and segmentation of brain lesions from 3D MR and CT images
type: CreativeWork
description: 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 …
abstract: 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 …
author: Mokhomo, Molise
language: eng
date published: 2014
date modified: 2026-07-22T22:23:24Z
provider: University of Cape Town
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Department of Electrical Engineering

PropertyValue
property ID: Handle
value: http://hdl.handle.net/11427/9089