Kernel Fisher discriminant for shape-based classification in epilepsy

Abstract

In this paper, we present the application of kernel Fisher discriminant in the statistical analysis of shape deformations that indicate the hemispheric location of an epileptic focus. The scans of two classes of patients with epilepsy, those with a right and those with a left anterior medial temporal lobe focus (RATL and LATL), as validated by clinical consensus and subsequent surgery, were compared to a set of age and sex matched healthy volunteers using both volume and shape based features. Shape-based features are derived from the displacement field characterizing the non-rigid deformation between the left and right hippocampi of a control or a patient as the case may be. Using the shape-based features, the results show a significant improvement in distinguishing between the controls and the rest (RATL and LATL) vis-a-vis volume-based features. Using a novel feature, namely, the normalized histogram of the 3D displacement field, we also achieved significant improvement over the volume-based feature in classifying the patients as belonging to either of the two classes LATL or RATL, respectively. It should be noted that automated identification of hemispherical foci of epilepsy has not been previously reported.