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

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Similarity-Based Pattern Analysis and Recognition

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

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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.)

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Notes

  1. 1.

    http://www.slicer.org/.

  2. 2.

    http://www.psychiatry.uiowa.edu/mhcrc/IPLpages/BRAINS.htm.

  3. 3.

    http://www.fil.ion.ucl.ac.uk/spm/.

  4. 4.

    http://www.fmrib.ox.ac.uk/fsl/.

  5. 5.

    In this section, we borrow the notation from [14, 73].

  6. 6.

    This is true once a family for the posterior distribution is given. See the original paper for details.

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Authors and Affiliations

  1. Departimento di Informatica, University of Verona, Verona, Italy

    Aydın Ulaş, Umberto Castellani, Manuele Bicego & Vittorio Murino

  2. Istituto Italiano di Tecnologia (IIT), Genova, Italy

    Vittorio Murino

  3. Department of Public Health and Community Medicine, Section of Psychiatry and Clinical Psychology, Inter-University Centre for Behavioural Neurosciences, University of Verona, Verona, Italy

    Marcella Bellani, Michele Tansella & Paolo Brambilla

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  1. Aydın Ulaş
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  1. DAIS, Ca' Foscari University of Venice, Venezia Mestre, Italy

    Marcello Pelillo

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Ulaş, A. et al. (2013). Analysis of Brain Magnetic Resonance (MR) Scans for the Diagnosis of Mental Illness. In: Pelillo, M. (eds) Similarity-Based Pattern Analysis and Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5628-4_10

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