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Geometry-Based Classification for Automated Schizophrenia Diagnosis

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Research in Data Science

Part of the book series: Association for Women in Mathematics Series ((AWMS,volume 17))

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Abstract

The improvement in medical imaging technologies has increased demand for automated diagnosis methodologies. In this paper, we propose a method for automated diagnosis of schizophrenia based on features extracted from segmented boundaries of the lateral projection of MRI images of the corpus callosum.

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Acknowledgements

The authors would like to thank Shantanu Joshi and Katherine Narr at UCLA for the data and the problem. NSF IIS-0954256 provided support during the initial phase of this project. Finally, we would like to thank the Association for Women in Mathematics and NSF (NSF-HRD 1500481) and ICERM for supporting the Women in the Science of Mathematics and Data collaboration workshop.

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Authors and Affiliations

  1. California State University, Channel Islands, Camarillo, CA, USA

    Robert Aroutiounian, Rosa Moreno & Robben Teufel

  2. Department of Computer Science, Occidental College, Los Angeles, CA, USA

    Kathryn Leonard

Authors
  1. Robert Aroutiounian
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  2. Kathryn Leonard
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  3. Rosa Moreno
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  4. Robben Teufel
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Union College, Schenectady, NY, USA

    Ellen Gasparovic

  2. Department of Computer Science, George Mason University, Fairfax, VA, USA

    Carlotta Domeniconi

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© 2019 The Author(s) and the Association for Women in Mathematics

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Aroutiounian, R., Leonard, K., Moreno, R., Teufel, R. (2019). Geometry-Based Classification for Automated Schizophrenia Diagnosis. In: Gasparovic, E., Domeniconi, C. (eds) Research in Data Science. Association for Women in Mathematics Series, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-030-11566-1_9

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