Identifying lesions in paediatric epilepsy using morphometric and textural analysis of magnetic resonance images

Abstract

We develop an image processing pipeline on Magnetic Resonance Imaging (MRI) sequences to identify features of Focal Cortical Dysplasia (FCD) in patients with MRIvisible FCD. We aim to use a computer-aided diagnosis system to identify epileptogenic lesions with a combination of established morphometric features and textural analysis using Gray-Level Co-occurrence Matrices (GLCM) on MRI sequences. The model will be validated on paediatric subjects. Preliminary morphometric analysis explored the use of computational models of established MRI features of FCD in aiding identification of subtle FCD on MRI-positive subjects. Following this, classification techniques were considered. The 2-Step Naive Bayes classifier was found to produce 100% subjectwise specificity and 94% subjectwise sensitivity (with 75% lesional specificity, 63% lesional sensitivity). Thus it correctly rejected 13/13 healthy subjects and colocalized lesions in 29/31 of the FCD cases with MRI visible lesions, with 63% coverage of the complete extent of the lesion using supplied lesional labels.