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International Conference on Computer Vision and Graphics

ICCVG 2018: Computer Vision and Graphics pp 329–340Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11114))

Abstract

Dental caries are currently one of the most prevalent diseases in the modern world. Early detection and diagnosis of the disease is the best treatment available to dental healthcare professionals and is crucial in preventing advanced stages of decay. This paper presents an effective model for caries detection across a variety of non-uniform X-rays using individual tooth segmentation, boundary detection and caries detection through image analysis techniques. The tooth segmentation is implemented using integral projection and an analytical division algorithm. The boundary detection is implemented through the use of top and bottom hat transformations and active contours. Finally the caries detection was achieved through the use of blob detection and cluster analysis on suspected carious regions. The cluster analysis generates its results relative to the image being analyzed and as such, forms the unsupervised evaluation approach of this paper. The viability of this unsupervised learning model, and its relative effectiveness of accurately diagnosing dental caries when compared to current systems, is indicated by the results detailed in this paper, with the proposed model achieving a 96% correct diagnostic.

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Author information

Authors and Affiliations

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

    Darren Osterloh & Serestina Viriri

Authors
  1. Darren Osterloh
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  2. Serestina Viriri
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Corresponding author

Correspondence to Serestina Viriri .

Editor information

Editors and Affiliations

  1. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Leszek J. Chmielewski

  2. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Ryszard Kozera

  3. Faculty of Applied Informatics and Mathematics, Warsaw University of Life Sciences, Warsaw, Poland

    Arkadiusz Orłowski

  4. Institute of Computer Science, Silesian University of Technology, Gliwice, Poland

    Konrad Wojciechowski

  5. Technion, Israel Institute of Technology, Haifa, Israel

    Alfred M. Bruckstein

  6. University of Groningen, Groningen, The Netherlands

    Nicolai Petkov

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Osterloh, D., Viriri, S. (2018). Unsupervised Caries Detection in Non-standardized Periapical Dental X-Rays. In: Chmielewski, L., Kozera, R., Orłowski, A., Wojciechowski, K., Bruckstein, A., Petkov, N. (eds) Computer Vision and Graphics. ICCVG 2018. Lecture Notes in Computer Science(), vol 11114. Springer, Cham. https://doi.org/10.1007/978-3-030-00692-1_29

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  • DOI: https://doi.org/10.1007/978-3-030-00692-1_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00691-4

  • Online ISBN: 978-3-030-00692-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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