On the classification of temporal lobe epilepsy using MR image appearance

Classification of neurological diseases based on image characteristics often requires extensive modeling and user intervention. While other techniques concentrate on specific structures, the novelty of the method presented here resides in its analysis of the grey-level appearance of large, non-specific Volumes of Interest (VOI) from T1 MRI data. No manual intervention is required other than the selection of the VOI. This work presents the methodological framework and preliminary results towards our aim of classifying normal subjects and patients with Temporal Lobe Epilepsy (TLE) within the Medial Temporal Lobe. For this purpose, principal component analysis is performed on a set of normal subjects for the creation of a multi-dimensional space representative of a normal population. New data for normal and TLE subjects are projected in this space, under the assumption that the distributions of the projections are not identical and can be used for classification. It is shown that linear discriminant analysis of the eigencoordinates of the projected data can be used to classify normals vs TLE with a 70% accuracy based on only 10 eigenvectors. This results can go up to 100% if all eigenvectors defining the grey-level space are used.