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Proceedings Paper

Automatic quantification framework to detect cracks in teeth
Author(s): Hina Shah; Pablo Hernandez; Francois Budin; Deepak Chittajallu; Jean-Baptiste Vimort; Rick Walters; André Mol; Asma Khan; Beatriz Paniagua
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Paper 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.

Paper Details

Date Published: 12 March 2018
PDF: 8 pages
Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 105781K (12 March 2018); doi: 10.1117/12.2293603
Show Author Affiliations
Hina Shah, Kitware, Inc. (United States)
Pablo Hernandez, Massey Univ. (New Zealand)
Francois Budin, Kitware, Inc. (United States)
Deepak Chittajallu, Kitware, Inc. (United States)
Jean-Baptiste Vimort, Kitware, Inc. (United States)
Rick Walters, The Univ. of North Carolina at Chapel Hill (United States)
André Mol, The Univ. of North Carolina at Chapel Hill (United States)
Asma Khan, The Univ. of North Carolina at Chapel Hill (United States)
Beatriz Paniagua, Kitware, Inc. (United States)


Published in SPIE Proceedings Vol. 10578:
Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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