Dissimilarity-based detection of schizophrenia

In this article, a novel approach to schizophrenia classi- fication using magnetic resonance images (MRI) is proposed. The presented method is based on dissimilarity-based classification tech- niques applied to morphological MRIs and diffusion-weighted images (DWI). Instead of working with features directly, pairwise dissimilar- ities between expert delineated regions of interest (ROIs) are consid- ered as representations based on which learning and classification can be performed. Experiments are carried out on a set of 59 patients and 55 controls and several pairwise dissimilarity measurements are analyzed. We demonstrate that significant improvements can be obtained when combining over different ROIs and different dissimilar- ity measures. We show that combining ROIs using the dissimilarity- based representation, we achieve higher accuracies. The dissimilar- ity-based representation outperforms the feature-based representa- tion in all cases. Best results are obtained by combining the two modalities. In summary, our contribution is threefold: (i) We introduce the usage of dissimilarity-based classification to schizophrenia detec- tion and show that dissimilarity-based classification achieves better results than normal features, (ii) We use dissimilarity combination to achieve better accuracies when carefully selected ROIs and dissimi- larity measures are considered, and (iii) We show that by combining multiple modalities we can achieve even better results.