![]() ![]() 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. ![]()
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