Fully automated classification of HARDI in vivo data using a support vector machine

Abstract

The purpose of this study is the classification of high angular resolution diffusion imaging (HARDI) in vivo data using a model-free approach. This is achieved by using a Support Vector Machine (SVM) algorithm taken from the field of supervised statistical learning. Six classes of image components are determined: grey matter, parallel neuronal fibre bundles in white matter, crossing neuronal fibre bundles in white matter, partial volume between white and grey matter, background noise and cerebrospinal fluid. The SVM requires properties derived from the data as input, the so called feature vector, which should be rotation invariant. For our application we derive such a description from the spherical harmonic decomposition of the HARDI signal. With this information the SVM is trained in order to find the function for separating the classes. The SVM is systematically tested with simulated data and then applied to six in vivo data sets. This new approach is data-driven and enables fully automatic HARDI data segmentation without employing a T1 MPRAGE scan and subjective expert intervention. This was demonstrated on five test in vivo data sets giving robust results. The segmentation results could be used as a priori knowledge for increasing the performance of fibre tracking as well as for other clinical and diagnostic applications of diffusion weighted imaging (DWI).