Mapping Brains on Grids of Features for Schizophrenia Analysis

  • Alessandro Perina
  • Denis Peruzzo
  • Maria Kesa
  • Nebojsa Jojic
  • Vittorio Murino
  • Mellani Bellani
  • Paolo Brambilla
  • Umberto Castellani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)


This paper exploits the embedding provided by the counting grid model and proposes a framework for the classification and the analysis of brain MRI images. Each brain, encoded by a count of local features, is mapped into a window on a grid of feature distributions. Similar sample are mapped in close proximity on the grid and their commonalities in their feature distributions are reflected in the overlap of windows on the grid. Here we exploited these properties to design a novel kernel and a visualization strategy which we applied to the analysis of schizophrenic patients. Experiments report a clear improvement in classification accuracy as compared with similar methods. Moreover, our visualizations are able to highlight brain clusters and to obtain a visual interpretation of the features related to the disease.


Support Vector Machine Schizophrenic Patient Cortical Thickness Latent Dirichlet Allocation Locally Linear Embedding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alessandro Perina
    • 1
  • Denis Peruzzo
    • 6
  • Maria Kesa
    • 2
  • Nebojsa Jojic
    • 3
  • Vittorio Murino
    • 1
    • 4
  • Mellani Bellani
    • 5
  • Paolo Brambilla
    • 7
  • Umberto Castellani
    • 4
  1. 1.Istituto Italiano di Tecnologia (IIT)GenovaItaly
  2. 2.Tallinn University of TechnologyTallinnEstonia
  3. 3.Microsoft ResearchRedmondUSA
  4. 4.Department of Computer ScienceUniversity of VeronaVeronaItaly
  5. 5.Department of PsychiatryUniversity of VeronaVeronaItaly
  6. 6.IRCCS “E. Medea” Scientific InstituteUdineItaly
  7. 7.Department of Experimental and Clinical Medical SciencesUniversity of UdineUdineItaly

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