Data di Pubblicazione:
2011
Citazione:
Dissimilarity-based detection of schizophrenia / A. Ulas, R. Duin, U. Castellani, M. Loog, P. Mirtuono, M. Bicego, V. Murino, M. Bellani, S. Cerruti, M. Tansella, P. Brambilla. - In: INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY. - ISSN 0899-9457. - 21:2(2011), pp. 179-192. [10.1002/ima.20279]
Abstract:
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.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
schizophrenia detection; dissimilarity-based classification; structural MRI; diffusion-weighted imaging
Elenco autori:
A. Ulas, R. Duin, U. Castellani, M. Loog, P. Mirtuono, M. Bicego, V. Murino, M. Bellani, S. Cerruti, M. Tansella, P. Brambilla
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