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Analysis of Brain Magnetic Resonance (MR) Scans for the Diagnosis of Mental Illness

  • Aydın Ulaş
  • Umberto Castellani
  • Manuele BicegoEmail author
  • Vittorio Murino
  • Marcella Bellani
  • Michele Tansella
  • Paolo Brambilla
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

We address the problem of schizophrenia detection by analyzing magnetic resonance imaging (MRI). In general, mental illness like schizophrenia or bipolar disorders are traditionally diagnosed by self-reports and behavioral observations. A new trend in neuroanatomical research consists of using MRI images to find possible connections between cognitive impairments and neuro-physiological abnormalities. Indeed, brain imaging techniques are appealing to provide a non-invasive diagnostic tool for mass analyses and early diagnoses. The problem is challenging due to the heterogeneous behavior of the disease and up to now, although the literature is large in this field, there is not a consolidated framework to deal with it. In this context, advanced pattern recognition and machine learning techniques can be useful to improve the automatization of the involved procedures and the characterization of mental illnesses with specific and detectable brain abnormalities. In this book, we have exploited similarity-based pattern recognition techniques to further improve brain classification problem by employing the algorithms developed in the other chapters of this book. (This chapter is based on previous works (Castellani et al. in Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI’11, vol. 6892, pp. 426–433, 2011; Gönen et al. in Proceedings of the International Workshop on Similarity-Based Pattern Analysis, SIMBAD’11, vol. 7005, pp. 250–260, 2011; Ulaş et al. in Proceedings of the Iberoamerican Congress on Pattern Recognition, CIARP’11, vol. 7042, pp. 491–498, 2011; in IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB’11, vol. 7036, pp. 306–317, 2011; and in Int. J. Imaging Syst. Technol. 21(2):179–192, 2011) by the authors and contains text, equations and experimental results taken from these papers.)

Keywords

Support Vector Machine Apparent Diffusion Coefficient Diffusion Weight Image Dissimilarity Measure Multiple Kernel Learn 
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-Verlag London 2013

Authors and Affiliations

  • Aydın Ulaş
    • 1
  • Umberto Castellani
    • 1
  • Manuele Bicego
    • 1
    Email author
  • Vittorio Murino
    • 1
    • 2
  • Marcella Bellani
    • 3
  • Michele Tansella
    • 3
  • Paolo Brambilla
    • 3
  1. 1.Departimento di InformaticaUniversity of VeronaVeronaItaly
  2. 2.Istituto Italiano di Tecnologia (IIT)GenovaItaly
  3. 3.Department of Public Health and Community Medicine, Section of Psychiatry and Clinical Psychology, Inter-University Centre for Behavioural NeurosciencesUniversity of VeronaVeronaItaly

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