Machine Learning Based Analysis of Structural MRI for Epilepsy Diagnosis

Epilepsy is a common neurological disorder, charactererized by abnormal firing of neurons. Magnetic Resonance Imaging (MRI) techniques can be integrated with machine learning methods to diagnose epileptic patients noninvasively. In this study, we use structural MRI data of 17 subjects (10 epileptic patients and 7 normal control subjects) and segment brain tissues using a Gram-Schmidt orthogonalization method and a unified tissue segmentation approach. We then compute first-order statistical and volumetric gray-level co-occurrence matrix (GLCM) texture features and train SVM classifiers for epilepsy diagnosis based on the features of the whole brain or those of the hippocampus. We achieve an accuracy of 94 % using the unified segmentation method and whole-brain analysis approach.