Our results show high discriminative specificity and sensitivity of this method. The crack detection was trained on the most significant wavelet coefficients at each scale using a bagged classifier of Support Vector Machines. The cracks were simulated using multiple orientations. These initial results were created using hr-CBCT scans of a set of healthy teeth and of teeth with simulated longitudinal cracks. 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. Currently used imaging modalities like Cone Beam Computed Tomography (CBCT) and intraoral radiography often have low sensitivity and do not show cracks clearly. Most cracks are not detected early because of the discontinuous symptoms and lack of good diagnostic tools. If detected early and accurately, patients can retain their teeth for a longer time. Studies show that cracked teeth are the third most common cause for tooth loss in industrialized countries. Shah, Hina Hernandez, Pablo Budin, Francois Chittajallu, Deepak Vimort, Jean-Baptiste Walters, Rick Mol, André Khan, Asma Paniagua, Beatriz Automatic quantification framework to detect cracks in teeth
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May 2023
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