Diagnosis of Brain Lesions, Glioma, Multiple-Sclerosis and Metastases from MRI: An efficient classifier-aided method using Refractive Index as a surrogate Biological Marker
DOI:
https://doi.org/10.14738/jbemi.53.4700Keywords:
Medical Imaging, Biomedical Engineering, Machine Learning, Brain Lesions, Glioma, Spectroscopy,Abstract
We introduce a highly accurate method of diagnosing the various pathological conditions that might exist in a subject’s brain, like edema, multiple sclerosis (Tumefactive, Relapsing-remitting, secondary-progressive, primary-progressive and progressive-relapsing), glioma, glioblastoma and metastases. These show up on conventional MRI scans, but it is often difficult to identify the exact type of the pathology from the grayscale image. We employ the use of Support Vector Machines (SVM) to work on the MR Spectroscopy [6, 12] data and correctly identify the condition-especially in seemingly vague cases where radiologists cannot rule out high uncertainty in their conclusion. The SVM trains on data sets collected for different patients and optimizes its hyperplanes based on eight input variables – T2, CHO, ADC, CR, CHO/NAA, CR/NAA, LIP/LAC, MI, CH/CR, T2 periphery [6] and Refractive index. Refractive index is an additional parameter which we include to get better boundary lines and accuracy, as shown in our prior works [10]. We test this SVM on a set of 19 patients’ data and achieve 100% accuracy in predictions. The training and testing is carried out in MATLAB.
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