3-D MRI Brain Scan Classification Using A Point Series Based Representation

This paper presents a procedure for the classification of 3-D objects in Magnetic Resonance Imaging (MRI) brain scan volumes. More specifically the classification of the left and right ventricles of the brain according to whether they feature epilepsy or not. The main contributions of the paper are two point series representation techniques to support the above: (i) Disc-based and (ii) Spoke-based. The proposed methods were evaluated using Support Vector Machine (SVM) and K-Nearest Neighbour (KNN) classifiers. The first required a feature space representation which was generated using Hough signature extraction. The second required some distance measure; the “warping path” distance generated using Dynamic Time Warping (DTW) curve comparison was used for this purpose. An epilepsy dataset used for evaluation purposes comprised 210 3-D MRI brain scans of which 105 were from “healthy” people and 105 from “epilepsy patients”. The results indicated that the proposed process can successfully be used to classify objects within 3-D data volumes.