Brain Tumor Detection Based on Magnetic Resonance Image Analysis
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Date
2018-01
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Addis Ababa University
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.
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Keywords
Brain Tumor Detection, Magnetic Resonance Image