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Unsupervised Caries Detection in Non-standardized Bitewing Dental X-Rays

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Advances in Visual Computing (ISVC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10072))

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Abstract

In recent years dental image processing has become a useful tool in aiding healthcare professionals diagnose patients. Despite advances in the field, accurate diagnoses are still problematic due to the non-uniform nature of dental X-rays. This is attributed to current systems utilizing a supervised learning model for their deterministic algorithm when identifying caries. This paper presents a method for the detection of caries across a variety of non-uniform X-ray images using an unsupervised learning model. This method aims to identify potential caries hallmarks within a tooth without comparing against a set of criteria learned from a database of images. The results show the viability of an unsupervised learning approach and the effectiveness of the method when compared to the supervised approaches.

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Author information

Authors and Affiliations

  1. School of Maths, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa

    D. Osterloh & S. Viriri

Authors
  1. D. Osterloh
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  2. S. Viriri
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Corresponding author

Correspondence to S. Viriri .

Editor information

Editors and Affiliations

  1. University of Nevada , Reno, Nevada, USA

    George Bebis

  2. NASA Ames Research Center , Moffett Field, California, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory , Berkeley, California, USA

    Bahram Parvin

  4. Desert Research Institute , Reno, Nevada, USA

    Darko Koracin

  5. The Australian National University , O'Malley, Aust Capital Terr, Australia

    Fatih Porikli

  6. Pilot AI Labs , Redwood City, California, USA

    Sandra Skaff

  7. University of Florida , Gainesville, Florida, USA

    Alireza Entezari

  8. Google Inc. , Mountain View, California, USA

    Jianyuan Min

  9. Osaka University , Osaka, Japan

    Daisuke Iwai

  10. The MOVES Institute , Monterey, California, USA

    Amela Sadagic

  11. University of Arizona , Tucson, Arizona, USA

    Carlos Scheidegger

  12. Université Paris-Sud , Orsay, France

    Tobias Isenberg

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© 2016 Springer International Publishing AG

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Osterloh, D., Viriri, S. (2016). Unsupervised Caries Detection in Non-standardized Bitewing Dental X-Rays. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2016. Lecture Notes in Computer Science(), vol 10072. Springer, Cham. https://doi.org/10.1007/978-3-319-50835-1_58

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  • DOI: https://doi.org/10.1007/978-3-319-50835-1_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50834-4

  • Online ISBN: 978-3-319-50835-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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