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Brain Tumor Detection Based on Magnetic Resonance Image Analysis

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dc.contributor.advisor Dawit, Assefa (PhD)
dc.contributor.author Amare, Ambaw
dc.date.accessioned 2019-01-04T07:11:34Z
dc.date.available 2019-01-04T07:11:34Z
dc.date.issued 2018-01
dc.identifier.uri http://localhost:80/xmlui/handle/123456789/15546
dc.description.abstract Automatic detection of brain tumors based on magnetic resonance (MR) image processing has been developed in this thesis. Improving the ability to accurately identify early-stage tumors is important goal for physicians, because early detection of brain tumors is a key factor in producing successful treatments. In this regard, an automatic brain tumor detection and segmentation framework has been proposed in this thesis work based on contrast enhanced T1 weighted (T1-W) images acquired from a cohort of patients with confirmed high grade brain tumors. Gray scale T1-W images have been represented in the three component Trinion space and Trinion Fourier transform has been applied aiming to extract useful features that could be used to automatically detect and segment brain tumors from their surrounding background. The performance of the proposed scheme has been evaluated by comparing its segmentation outputs with the ground truth information (based on manual contours by radiologists) that came with the MR data set. Results have showed that the algorithm achieved 99.6% sensitivity, 100% specificity, and 99.8% accuracy for pixel based segmentation while it achieved 91.5% sensitivity, 90% specificity and 90.5% accuracy for image based classification of tumors. en_US
dc.language.iso en_US en_US
dc.publisher Addis Ababa University en_US
dc.subject Brain Tumor Detection en_US
dc.subject Magnetic Resonance Image en_US
dc.title Brain Tumor Detection Based on Magnetic Resonance Image Analysis en_US
dc.type Thesis en_US

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