Advertisement

Journal of Signal Processing Systems

, Volume 90, Issue 1, pp 87–97 | Cite as

Boosting Proximal Dental Caries Detection via Combination of Variational Methods and Convolutional Neural Network

  • Joonhyang Choi
  • Hyunjun Eun
  • Changick Kim
Article
  • 305 Downloads

Abstract

Proximal dental caries are diagnosed using dental X-ray images. Unfortunately, the diagnosis of proximal dental caries is often stifled due to the poor quality of dental X-ray images. Therefore, we propose an automatic detection system to detect proximal dental caries in periapical images for the first time. The system comprises four modules: horizontal alignment of pictured teeth, probability map generation, crown extraction, and refinement. We first align the pictured teeth horizontally as a pre-process to minimize performance degradation due to rotation. Next, a fully convolutional network are used to produce a caries probability map while crown regions are extracted based on optimization schemes and an edge-based level set method. In the refinement module, the caries probability map is refined by the distance probability modeled by crown regions since caries are located near tooth surfaces. Also we adopt non-maximum suppression to improve the detection performance. Experiments on various periapical images reveal that the proposed system using a convolutional neural network (CNN) and crown extraction is superior to the system using a naïve CNN.

Keywords

Dental X-ray images Proximal dental caries Convolutional neural networks Dental image segmentation Variational methods 

Notes

Acknowledgments

This work is supported by Vatech Co., Ltd., Korea, for supporting the study and providing the dataset of dental X-ray images.

References

  1. 1.
    Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 3431–3440).Google Scholar
  2. 2.
    Li, C., Xu, C., Gui, C., & Fox, M.D. (2010). Distance regularized level set evolution and its application to image segmentation. IEEE Transactions on Image Processing, 19(12), 3243–3254.MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.CrossRefGoogle Scholar
  4. 4.
    Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 886–893).Google Scholar
  5. 5.
    Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In IEEE Conference on Computer Vision and Pattern Recognition (pp. 511–518).Google Scholar
  6. 6.
    Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 2037–2041.CrossRefMATHGoogle Scholar
  7. 7.
    Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.CrossRefGoogle Scholar
  8. 8.
    Lecun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Habbard, W., & Jackel, L.D. (1990). Handwritten digit recognition with a back-propagation network. In Proceedings of Conference on Neural Information Processing Systems (pp. 396–404).Google Scholar
  9. 9.
    Boureau, Y.L., Ponce, J., & Lecun, Y. (2010). A theoretical analysis of feature pooling in visual recognition. In Proceedings of International Conference on Machine Learning (pp. 111–118).Google Scholar
  10. 10.
    Krizhevsky, A., Sutskever, I., & Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In Proceedings of Conference on Neural Information Processing Systems (pp. 1097–1105).Google Scholar
  11. 11.
    Lawrence, S., Giles, C.L., Tsoi, A.C., & Back, A.D. (1997). Face recognition: a convolutional neural-network approach. Proceedings of Conference on Neural Information Processing Systems, 8(1), 98–113.Google Scholar
  12. 12.
    Cireşan, D.C., Giusti, A., Gambardella, L.M., & Schmidhuber, J. (2013). Mitosis detection in breast cancer histology images with deep neural networks. In Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 411–418).Google Scholar
  13. 13.
    Zhang, W., Li, R., Deng, H., Wang, L., Lin, W., Ji, S., & Shen, D. (2015). Deep convolutional neural networks for multi-modality isointense infant brain image segmentation. NeuroImage, 214–224.Google Scholar
  14. 14.
    Cireşan, D.C., Giusti, A., Gambardella, L.M., & Schmidhuber, J. (2012). Deep neural networks segment neuronal membranes in electron microscopy images. In Proceedings of Conference on Neural Information Processing Systems (pp. 2843–2851).Google Scholar
  15. 15.
    Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 248–255).Google Scholar
  16. 16.
    Taigman, Y., Yang, M., Ranzato, M.A., & Wolf, L. (2014). Deepface: Closing the gap to human-level performance in face verification. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (pp. 1701–1708).Google Scholar
  17. 17.
    Jain, A.K., & Chen, H. (2004). Matching of dental X-ray images for human identification. Pattern Recognition, 37(7), 1519– 1532.CrossRefGoogle Scholar
  18. 18.
    Fahmy, G., Nassar, D., Haj-Said, E., Chen, H., Nomir, O., Zhou, J., Howell, R., Ammar, H.H., Abdel-Mottaleb, M., & Jain, A.K. (2005). Toward an automated dental identification system. Journal of Electronic Imaging, 14(4), 043018.CrossRefGoogle Scholar
  19. 19.
    Zhou, J., & Abdel-Mottaleb, M. (2005). A content-based system for human identification based on bitewing dental X-ray images. Pattern Recognition, 38(11), 2132–2142.CrossRefGoogle Scholar
  20. 20.
    Nomir, O., & Abdel-Mottaleb, M. (2004). A system for human identification from X-ray dental radiographs. Pattern Recognition, 38(8), 1295–1305.CrossRefMATHGoogle Scholar
  21. 21.
    Mahoor, M.H., & Abdel-Mottaleb, M. (2005). Classification and numbering of teeth in dental bitewing images. Pattern Recognition, 38(4), 577–586.CrossRefGoogle Scholar
  22. 22.
    Lin, P.L., Lai, Y.H., & Huang, P.W. (2010). An effective classification and numbering system for dental bitewing radiographs using teeth region and contour information. Pattern Recognition, 43(4), 1380–1392.CrossRefGoogle Scholar
  23. 23.
    Kass, M., Witkin, A., & Terzopoulos, D. (1988). Snakes: Active contour models. International Journal of Computer Vision, 1(4), 321–331.CrossRefMATHGoogle Scholar
  24. 24.
    Al-sherif, N., Guo, G., & Ammar, H.H. (2012). A new approach to teeth segmentation. In Proceedings of IEEE International Symposium on Multimedia (pp. 1295–1305).Google Scholar
  25. 25.
    Avidan, S., & Shamir, A. (2007). Seam carving for content-aware image resizing. ACM Transactions on Graphics, 26(3), 10.CrossRefGoogle Scholar
  26. 26.
    Shah, S., Abaza, A., Ross, A., & Ammar, H. (2006). Automatic tooth segmentation using active contour without edges. In Proceedings of Biometrics Symposium: Special Session on Research (pp. 1–6).Google Scholar
  27. 27.
    Chan, T.F., & Vese, L. (2001). Active contours without edges. IEEE Transactions on Image Processing, 10(2), 266–277.CrossRefMATHGoogle Scholar
  28. 28.
    Osher, S., & Sethian, J.A. (1988). Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton- Jacobi formulations. Journal of Computational Physics, 79(1), 12–49.MathSciNetCrossRefMATHGoogle Scholar
  29. 29.
    Caselles, V., Catte, F., Coll, T., & Dibos, F. (1993). A geometric model for active contours in image processing. Numerische Mathematik, 66(1), 1–31.MathSciNetCrossRefMATHGoogle Scholar
  30. 30.
    Oliveira, J.P.R. (2009). Caries detection in panoramic dental X-ray images: Master Thesis, Universidade da Beira Interior Departamento de Informatica.Google Scholar
  31. 31.
    Rad, A.E., Amin, I.B.M., Rahim, M.S.M., & Kolivand, H. (2015). Computer-aided dental caries detection system from X-ray images. Computational Intelligence in Information Systems, 331, 233–243.Google Scholar
  32. 32.
    Otsu, N. (1979). A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.MathSciNetCrossRefGoogle Scholar
  33. 33.
    Fischler, M.A., & Bolles, R.C. (1981). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395.MathSciNetCrossRefGoogle Scholar
  34. 34.
    Nair, N., & Hinton, G.E. (2010). Rectified linear units improve restricted boltzmann machines. Inproceedings of International Conference on Machine Learning, 807–814.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Electrical EngineeringKorea Advanced Institute of Science and Technology (KAIST)DaejeonRepublic of Korea

Personalised recommendations