Local kernel for brains classification in schizophrenia

U. Castellani, E. Rossato, V. Murino, M. Bellani, G. Rambaldelli, M. Tansella, P. Brambilla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 classification rate has been obtained with this technique and the performance has improved up to 85% when the subjects have been stratified by sex.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages112-121
Number of pages10
Volume5883 LNAI
DOIs
Publication statusPublished - 2009
Event11th International Conference of the Italian Association for Artificial Intelligence: Emergent Perspectives in Artificial Intelligence, AI IA 2009 - Reggio Emilia, Italy
Duration: Dec 9 2009Dec 12 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5883 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th International Conference of the Italian Association for Artificial Intelligence: Emergent Perspectives in Artificial Intelligence, AI IA 2009
CountryItaly
CityReggio Emilia
Period12/9/0912/12/09

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Local kernel for brains classification in schizophrenia'. Together they form a unique fingerprint.

  • Cite this

    Castellani, U., Rossato, E., Murino, V., Bellani, M., Rambaldelli, G., Tansella, M., & Brambilla, P. (2009). Local kernel for brains classification in schizophrenia. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5883 LNAI, pp. 112-121). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5883 LNAI). https://doi.org/10.1007/978-3-642-10291-2_12