Advertisement

Extraction of 3D Shape of a Tooth from Dental CT Images with Region Growing Method

  • Ryuichi Yanagisawa
  • Shinichiro Omachi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6540)

Abstract

Dental information is useful for personal identification. In this paper, a method for extracting three-dimensional shape of a tooth automatically from dental CT images is proposed. In the previous method, one of the main issue is the mis-extraction of the adjacent region caused by the similarity of feature between a tooth and its adjacent teeth or the surrounding alveolar bone. It is important to extract an accurate shape of the target tooth as an independent part from the adjacent region. In the proposed method, after denoising, the target tooth is segmented to parts such as a shaft of a tooth or a dental enamel by the mean shift clustering. Then, some segments in the certain tooth is extracted as a certain region by the region growing method. Finally, the contour of the tooth is specified by applying the active contour method, and the shape of the tooth is extracted.

Keywords

tooth shape dental CT image region extraction region growing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Transaction on Pattern Analysis and Machine Intelligence 17, 790–799 (1995)CrossRefGoogle Scholar
  2. 2.
    Comaniciu, D.: Mean shift: a robust approach toward feature space analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)CrossRefGoogle Scholar
  3. 3.
    Grenier, T., Revol-Muller, C., Costes, N., Janier, M., Gimenez, G.: Automated seeds location for whole body NaF PET segmentation. IEEE Transaction on Nuclear Science 52, 1401–1405 (2005)CrossRefGoogle Scholar
  4. 4.
    Jain, A.K., Chen, H.: Matching of dental X-ray images for human identification. Pattern Recognition 37, 1519–1532 (2004)CrossRefGoogle Scholar
  5. 5.
    Kass, M., Witkin, A., Terzopilos, D.: Snakes: active contour models. International Journal of Computer Vision 1, 321–331 (1988)CrossRefGoogle Scholar
  6. 6.
    Omachi, S., Saito, K., Aso, H., Kasahara, S., Yamada, S., Kimura, K.: Tooth shape reconstruction from CT images using spline curves. In: Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, pp. 393–396 (2007)Google Scholar
  7. 7.
    Satake, K., Yamaji, Y., Yamaguchi, S., Tanaka, H.: Liver segmentation method for 3D non-contrast abdominal CT image based on the region growing and the probabilistic atlas. Technical Report of IEICE, PRMU2008-15 (2008)Google Scholar
  8. 8.
    Su, T., Funabiki, N., Kishimoto, E.: An improvement of a tooth contour extraction method and a tooth contour database design based on WEB applications. Technical Report of IEICE, PRMU2004-168 (2005)Google Scholar
  9. 9.
    Tomasi, C., Manduchi, R.: Bilateral filtering for gray and color images. In: Proceedings of the Sixth International Conference on Computer Vision, pp. 839–846 (1998)Google Scholar
  10. 10.
    Yokoyama, K., Kitasaka, T., Mori, K., Mekada, Y., Hasegawa, J., Toriwaki, J.: Liver region extraction from 3D abdominal X-ray CT images using distribution features of abdominal organs. Journal of Computer Aided Diagnosis of Medical Images 7, 48–58 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ryuichi Yanagisawa
    • 1
  • Shinichiro Omachi
    • 1
  1. 1.Graduate School of EngineeringTohoku UniversityJapan

Personalised recommendations