Fully Automatic Teeth Segmentation in Adult OPG Images

• Main Authors: Nicolás Vila Blanco, Inmaculada Tomás Carmona, María José Carreira Nouche
• Format: Article
• Language: English
• Published: MDPI AG 2018-09-01
• Series: Proceedings
• Subjects: teeth segmentation; panoramic dental images; random forest regression-voting; machine learning
• Online Access: http://www.mdpi.com/2504-3900/2/18/1199

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.