Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Automatic teeth segmentation in cephalometric X-ray images using a coupled shape model

: Wirtz, Andreas; Wambach, Johannes; Wesarg, Stefan


Stoyanov, Danail (Ed.):
OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis : First International Workshop, OR 2.0 2018, 5th International Workshop, CARE 2018, 7th International Workshop, CLIP 2018, Third International Workshop, ISIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 and 20, 2018, Proceedings
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 11041)
ISBN: 978-3-030-01200-7 (Print)
ISBN: 978-3-030-01201-4 (Online)
ISBN: 3-030-01200-X
International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0) <1, 2018, Granada>
International Workshop on Computer Assisted and Robotic Endoscopy (CARE) <5, 2018, Granada>
International Workshop on Clinical Image-based Procedures (CLIP) <7, 2018, Granada>
International Workshop on Skin Image Analysis (ISIC) <7, 2018, Granada>
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) <21, 2018, Granada>
Fraunhofer IGD ()
Guiding Theme: Individual Health; Research Area: Computer vision (CV); Research Area: Modeling (MOD); dental imaging; statistical shape model (SSM); model based segmentations; automatic segmentation

Cephalometric analysis is an important tool used by dentists for diagnosis and treatment of patients. Tools that could automate this time consuming task would be of great assistance. In order to provide the dentist with such tools, a robust and accurate identification of the necessary landmarks is required. However, poor image quality of lateral cephalograms like low contrast or noise as well as duplicate structures resulting from the way these images are acquired make this task difficult. In this paper, a fully automatic approach for teeth segmentation is presented that aims to support the identification of dental landmarks. A 2-D coupled shape model is used to capture the statistical knowledge about the teeth’s shape variation and spatial relation to enable a robust segmentation despite poor image quality. 14 individual teeth are segmented and labeled using gradient image features and the quality of the generated results is compared to manually created gold-standard segmentations. Experimental results on a set of 14 test images show promising results with a DICE overlap of 77.2% and precision and recall values of 82.3% and 75.4%, respectively.