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AIIA
2009
Springer

Local Kernel for Brains Classification in Schizophrenia

9 years 8 months ago
Local Kernel for Brains Classification in Schizophrenia
Abstract. In this paper a novel framework for brain classification is proposed in the context of mental health research. A learning by example method is introduced by combining local measurements with non linear Support Vector Machine. Instead of considering a voxel-by-voxel comparison between patients and controls, we focus on landmark points which are characterized by local region descriptors, namely Scale Invariance Feature Transform (SIFT). Then, matching is obtained by introducing the local kernel for which the samples are represented by unordered set of features. Moreover, a new weighting approach is proposed to take into account the discriminative relevance of the detected groups of features. Experiments have been performed including a set of 54 patients with schizophrenia and 54 normal controls on which region of interest (ROI) have been manually traced by experts. Preliminary results on Dorsolateral PreFrontal Cortex (DLPFC) region are promising since up to 75% of successful c...
Umberto Castellani, E. Rossato, Vittorio Murino, M
Added 12 Aug 2010
Updated 12 Aug 2010
Type Conference
Year 2009
Where AIIA
Authors Umberto Castellani, E. Rossato, Vittorio Murino, M. Bellani, G. Rambaldelli, M. Tansella, P. Brambilla
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