Fully Automatic Teeth Segmentation in Adult OPG Images

Abstract

In this work, the problem of segmenting teeth in panoramic dental images is addressed. The Random Forest Regression Voting Constrained Local Models (RFRV-CLM) are used to perform the segmentation in two steps. Firstly, a set of mandible and teeth keypoints are located, and then that points are used to initialise each individual tooth model. A method to detect missing teeth based on the quality of fit is presented. The system is evaluated using 346 manually annotated images containing adult-stage teeth. Encouraging results on detecting missing teeth are achieved. The system is able to locate the outline of the teeth to a median point-to-curve error of 0.2 mm.

Keywords

• teeth segmentation
• panoramic dental images
• random forest regression-voting
• machine learning