Brain Tumor Detection Based on Magnetic Resonance Image Analysis
| dc.contributor.advisor | Dawit, Assefa (PhD) | |
| dc.contributor.author | Amare, Ambaw | |
| dc.date.accessioned | 2019-01-04T07:11:34Z | |
| dc.date.accessioned | 2023-11-04T15:22:09Z | |
| dc.date.available | 2019-01-04T07:11:34Z | |
| dc.date.available | 2023-11-04T15:22:09Z | |
| dc.date.issued | 2018-01 | |
| 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.identifier.uri | http://etd.aau.edu.et/handle/123456789/15546 | |
| 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 |