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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Transaction on Pattern Analysis and Machine Intelligence 17, 790–799 (1995)
Comaniciu, D.: Mean shift: a robust approach toward feature space analysis. IEEE Transaction on Pattern Analysis and Machine Intelligence 24, 603–619 (2002)
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)
Jain, A.K., Chen, H.: Matching of dental X-ray images for human identification. Pattern Recognition 37, 1519–1532 (2004)
Kass, M., Witkin, A., Terzopilos, D.: Snakes: active contour models. International Journal of Computer Vision 1, 321–331 (1988)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yanagisawa, R., Omachi, S. (2011). Extraction of 3D Shape of a Tooth from Dental CT Images with Region Growing Method. In: Sako, H., Franke, K.Y., Saitoh, S. (eds) Computational Forensics. IWCF 2010. Lecture Notes in Computer Science, vol 6540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19376-7_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-19376-7_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19375-0
Online ISBN: 978-3-642-19376-7
eBook Packages: Computer ScienceComputer Science (R0)