Automatic detection and classification of teeth in CT data

Authors:
Nguyen The Duy;Hans Lamecker;Dagmar Kainmueller;Stefan Zachow
Affiliations:
Zuse-Institute Berlin, Germany;Zuse-Institute Berlin, Germany;Zuse-Institute Berlin, Germany;Zuse-Institute Berlin, Germany
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Year:
2012

Citing 7
Cited 0

Automatic Tooth Region Separation for Dental CT Images

ICCIT '08 Proceedings of the 2008 Third International Conference on Convergence and Hybrid Information Technology - Volume 01
Automatic Extraction of Mandibular Nerve and Bone from Cone-Beam CT Data

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information

Pattern Recognition
Classification and numbering of teeth in dental bitewing images

Pattern Recognition
An automatic segmentation and reconstruction of mandibular structures from CT-data

IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Automatic segmentation of jaw tissues in CT using active appearance models and semi-automatic landmarking

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Automatic Construction of Dental Charts for Postmortem Identification

IEEE Transactions on Information Forensics and Security

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a fully automatic method for tooth detection and classification in CT or cone-beam CT image data. First we compute an accurate segmentation of the maxilla bone. Based on this segmentation, our method computes a complete and optimal separation of the row of teeth into 16 subregions and classifies the resulting regions as existing or missing teeth. This serves as a prerequisite for further individual tooth segmentation. We show the robustness of our approach by providing extensive validation on 43 clinical head CT scans.