Wirtz, Andreas ; Wambach, Johannes ; Wesarg, Stefan (2018)
Automatic Teeth Segmentation in Cephalometric X-Ray Images Using a Coupled Shape Model.
International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0). Granada, Spain
doi: 10.1007/978-3-030-01201-4_21
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2018 |
| Autor(en): | Wirtz, Andreas ; Wambach, Johannes ; Wesarg, Stefan |
| Art des Eintrags: | Bibliographie |
| Titel: | Automatic Teeth Segmentation in Cephalometric X-Ray Images Using a Coupled Shape Model |
| Sprache: | Englisch |
| Publikationsjahr: | 2018 |
| Ort: | Cham |
| Verlag: | Springer |
| Buchtitel: | OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis |
| Reihe: | Lecture Notes in Computer Science (LNCS) |
| Band einer Reihe: | 11041 |
| Veranstaltungstitel: | International Workshop on OR 2.0 Context-Aware Operating Theaters (OR 2.0) |
| Veranstaltungsort: | Granada, Spain |
| DOI: | 10.1007/978-3-030-01201-4_21 |
| URL / URN: | https://doi.org/10.1007/978-3-030-01201-4_21 |
| Kurzbeschreibung (Abstract): | 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. |
| Freie Schlagworte: | Dental imaging, Statistical shape models (SSM), Model based segmentations, Automatic segmentation |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 19 Jun 2019 11:19 |
| Letzte Änderung: | 19 Jun 2019 11:19 |
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