Browsing by Author "Chernet, Teferi"

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    Holistic Multi-Parametric Magnetic Resonance Image Processing for Identification of Neoplasm and Normal Brain Tissues
    (Addis Ababa University, 2016-12) Chernet, Teferi; Assefa, Dawit (PhD.)
    Abreakthrough advance in Magnetic Resonance Imaging (MRI) into standard clinical practices has enabled greater accuracy in delineating tumor volumes. The technique allows autonomous detection through noninvasive imaging of malignant brain tumors like gliomas and similar other body tumors for the requirement of efficient and effective procedures like biopsy and radiation therapy as well as for quantification of patients’ responses to treatment. The role of MR imaging in regard of characterization schemes of neoplasms has been tremendous. There are many approaches suggested in the literature for this purpose, one of which is color processing concept. Most color images have three components and each pixel/voxel on such color images can be represented as a three-component vector. Analogously, an MRI voxel, for example, can be seen as a vector with as many channels as available MR image parameters: T1, T2, PD, ADC, rcbv, Ktrans, T2*, etc. And there can be various methods of representing such images as colors. Hence using these methodologies and by combining multiple MRI parameters as we need, we can maximize the information we want to display. The research study exploits advanced mathematical and image analysis assets for use in analysis of MP-MR medical images making use of algorithmic procedures. Accuracy and potentials of the method is compared against gold standards (available ground truth). The data used is composed of standard co-registered multiple MR image parameters taken from patients treated for the highest grade and most aggressive of the gliomas. This approach comes with a great potential to assist accurate and effective detection, visualization, and analysis of useful medical image information for physicians and/or researchers as well. Even though MR images are the subjects of interest in this study, in principle the method developed could be applied to other medical images with multiple components acquired through different modalities. Keywords: Quaternion Fourier Transform, Trinion Fourier Transform, Image Processing, Principal Component Analysis.