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Pacific Rim International Conference on Artificial Intelligence

PRICAI 2016: PRICAI 2016: Trends in Artificial Intelligence pp 543–555Cite as

3-D Volume of Interest Based Image Classification

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9810))

Abstract

This paper proposes a number of techniques for 3-D image classification according to the nature of a particular Volume of Interest (VOI) that appears across a given image set. Three VOI Based Image Classification (VOIBIC) approaches are considered: (i) Statistical metric based, (ii) Point series based and (iii) Tree based. For evaluation purpose, two 3-D MRI brain scan datasets, Epilepsy and Musicians, were used; the aim being to distinguish between: (i) epilepsy patients versus healthy people and (ii) musicians versus non-musicians. The paper also considers augmenting the VOI data with meta data. According to the reported experimental results the Point series based approach, augmented with meta data, is the most effective.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Liverpool, Liverpool, UK

    Akadej Udomchaiporn & Frans Coenen

  2. Department of Biostatistics, University of Liverpool, Liverpool, UK

    Marta García-Fiñana

  3. School of Health Science, University of Liverpool, Liverpool, UK

    Vanessa Sluming

Authors
  1. Akadej Udomchaiporn
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  2. Frans Coenen
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  3. Marta García-Fiñana
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  4. Vanessa Sluming
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Corresponding author

Correspondence to Akadej Udomchaiporn .

Editor information

Editors and Affiliations

  1. Cardiff University, Cardiff, United Kingdom

    Richard Booth

  2. Southeast University , Nanjing, China

    Min-Ling Zhang

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© 2016 Springer International Publishing Switzerland

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Udomchaiporn, A., Coenen, F., García-Fiñana, M., Sluming, V. (2016). 3-D Volume of Interest Based Image Classification. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_45

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  • DOI: https://doi.org/10.1007/978-3-319-42911-3_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42910-6

  • Online ISBN: 978-3-319-42911-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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