In this article, a novel approach to schizophrenia classification using magnetic resonance images (MRI) is proposed. The presented method is based on dissimilarity-based classification techniques applied to morphological MRIs and diffusion-weighted images (DWI). Instead of working with features directly, pairwise dissimilarities between expert delineated regions of interest (ROIs) are considered 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 dissimilarity measures. We show that combining ROIs using the dissimilarity-based representation, we achieve higher accuracies. The dissimilarity-based representation outperforms the feature-based representation 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 detection 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 dissimilarity measures are considered, and (iii) We show that by combining multiple modalities we can achieve even better results

Dissimilarity-based detection of schizophrenia.

CASTELLANI, Umberto;MIRTUONO, Pasquale;BICEGO, Manuele;MURINO, Vittorio;BELLANI, Marcella;CERRUTI, Stefania;TANSELLA, Michele;
2011-01-01

Abstract

In this article, a novel approach to schizophrenia classification using magnetic resonance images (MRI) is proposed. The presented method is based on dissimilarity-based classification techniques applied to morphological MRIs and diffusion-weighted images (DWI). Instead of working with features directly, pairwise dissimilarities between expert delineated regions of interest (ROIs) are considered 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 dissimilarity measures. We show that combining ROIs using the dissimilarity-based representation, we achieve higher accuracies. The dissimilarity-based representation outperforms the feature-based representation 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 detection 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 dissimilarity measures are considered, and (iii) We show that by combining multiple modalities we can achieve even better results
schizophrenia detection; dissimilarity-based classification; structural MRI; diffusion-weighted imaging
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/363240
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 19
  • ???jsp.display-item.citation.isi??? 16
social impact