Advertisement

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.

Keywords

Coupled shape model Automatic segmentation Cephalometric dental X-ray image 

Notes

Acknowledgements

We thank Dr. Jan H. Willmann, University Hospital of Düsseldorf for providing the cephalometric images used in this work.

References

  1. 1.
    Arik, S., Ibragimov, B., Xing, L.: Fully automated quantitative cephalometry using convolutional neural networks. J. Med. Imaging 4, 1–11 (2017)CrossRefGoogle Scholar
  2. 2.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Training models of shape from sets of examples. In: Hogg, D., Boyle, R. (eds.) BMVC92, pp. 9–18. Springer, London (1992).  https://doi.org/10.1007/978-1-4471-3201-1_2CrossRefGoogle Scholar
  3. 3.
    Grau, V., Alcaiz, M., Juan, M., Monserrat, C., Knoll, C.: Automatic localization of cephalometric landmarks. J. Biomed. Inform. 34(3), 146–156 (2001)CrossRefGoogle Scholar
  4. 4.
    Heimann, T., Meinzer, H.P.: Statistical shape models for 3D medical image segmentation: a review. Med. Image Anal. 13(4), 543–563 (2009)CrossRefGoogle Scholar
  5. 5.
    Jain, A.K., Chen, H.: Matching of dental X-ray images for human identification. Pattern Recognit. 37(7), 1519–1532 (2004)CrossRefGoogle Scholar
  6. 6.
    Lindner, C., Wang, C.W., Huang, C.T., Li, C.H., Chang, S.W., Cootes, T.F.: Fully automatic system for accurate localisation and analysis of cephalometric landmarks in lateral cephalograms. Sci. Rep. 6, 33581 (2016)CrossRefGoogle Scholar
  7. 7.
    McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Med. Image Anal. 1(2), 91–108 (1996)CrossRefGoogle Scholar
  8. 8.
    Steger, S., Jung, F., Wesarg, S.: Personalized articulated atlas with a dynamic adaptation strategy for bone segmentation in CT or CT/MR head and neck images. In: Medical Imaging 2014: Image Processing. vol. 9034, p. 90341I. International Society for Optics and Photonics (2014)Google Scholar
  9. 9.
    Wang, C.W., et al.: A benchmark for comparison of dental radiography analysis algorithms. Med. Image Anal. 31, 63–76 (2016)CrossRefGoogle Scholar
  10. 10.
    Wirtz, A., Mirashi, S.G., Wesarg, S.: Automatic teeth segmentation in panoramic X-ray images using a coupled shape model in combination with a neural network (2018, accepted for publication at MICCAI 2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Andreas Wirtz
    • 1
    • 2
    Email author
  • Johannes Wambach
    • 1
  • Stefan Wesarg
    • 1
    • 2
  1. 1.Fraunhofer IGDDarmstadtGermany
  2. 2.Interactive Graphics Systems GroupTU DarmstadtDarmstadtGermany

Personalised recommendations