Skip to main content
SpringerLink
Log in

Automatic Tooth Segmentation and 3D Reconstruction from Panoramic and Lateral Radiographs

  • Conference paper
  • First Online:
Pattern Recognition and Computer Vision (PRCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12305))

Included in the following conference series:

  • 2588 Accesses

Abstract

The panoramic and lateral radiographs are commonly used in orthodontic dentistry to acquire patient-specific tooth morphology for diagnosing and treatment planning. Considering the variational dentition configurations and image blurs caused by device-specific artefacts and structure overlapping, the robust tooth segmentation and 3D reconstruction from radiographs remain a challenging issue. We propose a deformable exemplar-based conditional random fields (CRF) model for tooth segmentation and 3D shape estimation from the panoramic and lateral radiographs. The shared tooth foreground in the lateral and the panoramic radiographs are utilized for consistent labeling. The 3D deformable exemplars are introduced to provide a regularization of tooth contours to improve the tooth parsing in noisy and ambiguous radiographs. We introduce an alternating optimization scheme to solve the discrete superpixel labels and the continuous deformation of the 3D exemplars simultaneously. Extensive experiments on clinically obtained radiographs demonstrate that the proposed approach is effective and efficient for both tooth segmentation and 3D shape estimation from the panoramic and lateral radiographs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)

    Article  Google Scholar 

  2. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 33(5), 898–916 (2011)

    Article  Google Scholar 

  3. Audet, C., Dennis Jr., J.E.: Analysis of generalized pattern searches. SIAM J. Optim. 13(3), 889–903 (2002)

    Article  MathSciNet  Google Scholar 

  4. Baka, N., et al.: 2D–3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Med. Image Anal. 15(6), 840 (2011)

    Article  Google Scholar 

  5. Benameur, S., Mignotte, M., Labelle, H., De Guise, J.: A hierarchical statistical modeling approach for the unsupervised 3-D biplanar reconstruction of the scoliotic spine. IEEE Trans. Biomed. Eng. 52(12), 2041–2057 (2005)

    Article  Google Scholar 

  6. Benameur, S., Mignotte, M., Parent, S., Labelle, H., Skalli, W., de Guise, J.: 3D/2D registration and segmentation of scoliotic vertebrae using statistical models. Comput. Med. Imaging Graph. 27(5), 321–337 (2003)

    Article  Google Scholar 

  7. Chin, C., Lin, J., Wei, C., Hsu, M.: Dentition labeling and root canal recognition using ganand rule-based system. In: 2019 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 1–6 (2019)

    Google Scholar 

  8. Edwards, G.J., Taylor, C.J., Cootes, T.F.: Interpreting face images using active appearance models. In: Proceedings of the Automatic Face and Gesture Recognition 1998, pp. 300–305 (1998)

    Google Scholar 

  9. Fulkerson, B., Vedaldi, A., Soatto, S.: Class segmentation and object localization with superpixel neighborhoods. In: IEEE International Conference on Computer Vision, pp. 670–677. IEEE (2009)

    Google Scholar 

  10. Jader, G., Fontineli, J., Ruiz, M., Abdalla, K., Pithon, M.M., Oliveira, L.: Deep instance segmentation of teeth in panoramic X-ray images. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 400–407 (2018)

    Google Scholar 

  11. Jain, A.K., Chen, H.: Matching of dental X-ray images for human identification. Pattern Recogn. 37(7), 1519–1532 (2004)

    Article  Google Scholar 

  12. Kang, X., Yau, W.P., Taylor, R.H.: Simultaneous pose estimation and patient-specific model reconstruction from single image using maximum penalized likelihood estimation (MPLE). Pattern Recogn. 57, 61–69 (2016)

    Article  Google Scholar 

  13. Lai, Y.H., Lin, P.L.: Effective segmentation for dental X-ray images using texture-based fuzzy inference system. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 936–947. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88458-3_85

    Chapter  Google Scholar 

  14. Li, S., Fevens, T., Krzyżak, A., Li, S.: An automatic variational level set segmentation framework for computer aided dental X-rays analysis in clinical environments. Comput. Med. Imaging Graph. 30(2), 65–74 (2006)

    Article  Google Scholar 

  15. Lin, P.L., Huang, P.Y., Huang, P.W.: An automatic lesion detection method for dental X-ray images by segmentation using variational level set. In: International Conference on Machine Learning and Cybernetics (ICMLC), vol. 5, pp. 1821–1825. IEEE (2012)

    Google Scholar 

  16. Lin, P., Huang, P., Huang, P., Hsu, H., Chen, C.: Teeth segmentation of dental periapical radiographs based on local singularity analysis. Comput. Methods Programs Biomed. 113(2), 433–445 (2014)

    Article  Google Scholar 

  17. Milborrow, S., Nicolls, F.: Locating facial features with an extended active shape model. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5305, pp. 504–513. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88693-8_37

    Chapter  Google Scholar 

  18. Mukherjee, L., Singh, V., Dyer, C.R.: Half-integrality based algorithms for cosegmentation of images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2028–2035. IEEE (2009)

    Google Scholar 

  19. Pei, Y., Liu, B., Zha, H., Han, B., Xu, T.: Anatomical structure sketcher for cephalograms by bimodal deep learning. In: BMVC 2013 (2013)

    Google Scholar 

  20. Pei, Y., et al.: Personalized tooth shape estimation from radiograph and cast. IEEE Trans. Biomed. Eng. 59(9), 2400–2411 (2012)

    Article  Google Scholar 

  21. Platt, J., et al.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers, vol. 10, no. 3, pp. 61–74 (1999)

    Google Scholar 

  22. Pushparaj, V., Gurunathan, U., Arumugam, B.: An effective dental shape extraction algorithm using contour information and matching by Mahalanobis distance. J. Digit. Imaging 26(2), 259–268 (2013)

    Article  Google Scholar 

  23. Rad, A.E., Mohd Rahim, M.S., Rehman, A., Altameem, A., Saba, T.: Evaluation of current dental radiographs segmentation approaches in computer-aided applications. IETE Tech. Rev. 30(3), 210–222 (2013)

    Article  Google Scholar 

  24. Ronneberger, O., Fischer, P., Brox, T.: Dental X-ray image segmentation using a U-shaped deep convolutional network. In: ISBI (2015)

    Google Scholar 

  25. Rother, C., Minka, T., Blake, A., Kolmogorov, V.: Cosegmentation of image pairs by histogram matching-incorporating a global constraint into MRFs. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 993–1000. IEEE (2006)

    Google Scholar 

  26. Rother, C., Kolmogorov, V., Lempitsky, V., Szummer, M.: Optimizing binary MRFs via extended roof duality. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8. IEEE (2007)

    Google Scholar 

  27. Saad, A., El-Bialy, A., Kandil, A., Sayed, A.: Automatic cephalometric analysis using active appearance model and simulated annealing. Int. J. Graphics Vision Image Process. 6, 51–67 (2006). Special Issue on Image Retrieval and Representation

    Google Scholar 

  28. Said, E.H., Nassar, D.E.M., Fahmy, G., Ammar, H.H.: Teeth segmentation in digitized dental X-ray films using mathematical morphology. IEEE Trans. Inf. Forensics Secur. 1(2), 178–189 (2006)

    Article  Google Scholar 

  29. Shah, S., Abaza, A., Ross, A., Ammar, H.: Automatic tooth segmentation using active contour without edges. In: Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference, pp. 1–6. IEEE (2006)

    Google Scholar 

  30. Silva, G., Oliveira, L., Pithon, M.: Automatic segmenting teeth in X-ray images: trends, a novel data set, benchmarking and future perspectives. Expert Syst. Appl. 107, 15–31 (2018)

    Article  Google Scholar 

  31. Sumner, R., Schmid, J., Pauly, M.: Embedded deformation for shape manipulation. ACM Trans. Graph. (TOG) 26(3), 80 (2007)

    Article  Google Scholar 

  32. Yue, W., Yin, D., Li, C., Wang, G., Xu, T.: Automated 2-D cephalometric analysis on X-ray images by a model-based approach. IEEE Trans. Biomed. Eng. 53(8), 1615–1623 (2006)

    Article  Google Scholar 

  33. Zheng, G., et al.: Accurate and robust reconstruction of a surface model of the proximal femur from sparse-point data and a dense-point distribution model for surgical navigation. IEEE Trans. Biomed. Eng. 54(12), 2109–2122 (2007)

    Article  Google Scholar 

  34. Zheng, G.: Effective incorporating spatial information in a mutual information based 3D–2D registration of a CT volume to X-ray images. Comput. Med. Imaging Graph. 34(7), 553–562 (2010)

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by NSFC 61876008.

Author information

Authors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Department of Machine Intelligence, Peking University, Beijing, China

    Mochen Yu, Diya Sun & Yuru Pei

  2. Luoyang Institute of Science and Technology, Luoyang, China

    Yuke Guo

  3. School of Stomatology, Peking University, Beijing, China

    Tianmin Xu

Authors
  1. Mochen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuke Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diya Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuru Pei
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tianmin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuru Pei .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Yuxin Peng

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Qingshan Liu

  3. Dalian University of Technology, Dalian, China

    Huchuan Lu

  4. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences, Beijing, China

    Chenglin Liu

  6. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Peking University, Beijing, China

    Hongbin Zha

  8. Nanjing University of Science and Technology, Nanjing, China

    Jian Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yu, M., Guo, Y., Sun, D., Pei, Y., Xu, T. (2020). Automatic Tooth Segmentation and 3D Reconstruction from Panoramic and Lateral Radiographs. In: Peng, Y., et al. Pattern Recognition and Computer Vision. PRCV 2020. Lecture Notes in Computer Science(), vol 12305. Springer, Cham. https://doi.org/10.1007/978-3-030-60633-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60633-6_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60632-9

  • Online ISBN: 978-3-030-60633-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation