In studies using dental X-ray images, tooth localization is essential to produce accurate results. In this paper, we propose a tooth localization method that tightly localize diverse teeth in periapical dental X-ray images. Oriented tooth proposals are generated by using teeth separation lines (TSLs) and a tooth top line, which are reliable and tight to teeth. To classify each tooth proposal into either a tooth or a non-tooth, we utilize a convolutional neural network (CNN). Our CNN model is trained with three classes, i.e., one negative and two positives, for better classification. In addition, we propose scale based non-maximum suppression by integrating scale confidence with non-maximum suppression to efficiently eliminate multiple tooth localizations.
['Hyunjun Eun', 'Changick Kim']
Oriented tooth localization for periapical dental X-ray images via convolutional neural network