Automatic quantification framework to detect cracks in teeth

Hina Shah, Pablo Hernandez, Francois Budin, Deepak Chittajallu, Jean Baptiste Vimort, Rick Walters, André Mol, Asma Khan, Beatriz Paniagua

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Studies show that cracked teeth are the third most common cause for tooth loss in industrialized countries. If detected early and accurately, patients can retain their teeth for a longer time. Most cracks are not detected early because of the discontinuous symptoms and lack of good diagnostic tools. Currently used imaging modalities like Cone Beam Computed Tomography (CBCT) and intraoral radiography often have low sensitivity and do not show cracks clearly. This paper introduces a novel method that can detect, quantify, and localize cracks automatically in high resolution CBCT (hr-CBCT) scans of teeth using steerable wavelets and learning methods. These initial results were created using hr-CBCT scans of a set of healthy teeth and of teeth with simulated longitudinal cracks. The cracks were simulated using multiple orientations. The crack detection was trained on the most significant wavelet coefficients at each scale using a bagged classifier of Support Vector Machines. Our results show high discriminative specificity and sensitivity of this method. The framework aims to be automatic, reproducible, and open-source. Future work will focus on the clinical validation of the proposed techniques on different types of cracks ex-vivo. We believe that this work will ultimately lead to improved tracking and detection of cracks allowing for longer lasting healthy teeth.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor Gimi, Andrzej Krol
PublisherSPIE
ISBN (Electronic)9781510616455
DOIs
StatePublished - Jan 1 2018
Externally publishedYes
EventMedical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging - Houston, United States
Duration: Feb 11 2018Feb 13 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10578
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
CountryUnited States
CityHouston
Period2/11/182/13/18

Keywords

  • High-resolution Cone Beam Computed Tomography
  • Machine learning
  • Tooth fracture detection
  • Wavelet analysis

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Shah, H., Hernandez, P., Budin, F., Chittajallu, D., Vimort, J. B., Walters, R., Mol, A., Khan, A., & Paniagua, B. (2018). Automatic quantification framework to detect cracks in teeth. In B. Gimi, & A. Krol (Eds.), Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging [105781K] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10578). SPIE. https://doi.org/10.1117/12.2293603