Automatic Classification of Teeth in Bitewing Dental Images Using OLPP

Abstract

Teeth classification is an important component in building an Automated Dental Identification System (ADIS) as part of creating a data structure that guides tooth-to-tooth matching. This aids in avoiding illogical comparisons that both inefficiently consume the limited computational resources and mislead decision-making. We tackle this problem by using low computational-cost, appearance-based Orthogonal Locality Preserving Projection (OLPP) algorithm to assign an initial class, i.e. molar or premolar to the teeth in bitewing dental images. After this initial classification, we use a string matching technique, based on teeth neighborhood rules, to validate initial teeth-classes and thus assign each tooth a number corresponding to its location in the dental chart. On a large dataset of bitewing films that contain 622 teeth, the proposed approach achieves classification accuracy of 89% and teeth class validation enhances the overall teeth classification accuracy to 92%.