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Scandinavian Journal of Forensic Science

The Journal of Danish Society of Forensic Medicine

2 Issues per year

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2353-0707
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Automated Dental Identification with Lowest Cost Path-Based Teeth and Jaw Separation

Jan-Vidar Ølberg / Morten Goodwin
Published Online: 2017-03-04 | DOI: https://doi.org/10.1515/sjfs-2016-0008

Abstract

Teeth are some of the most resilient tissues of the human body. Because of their placement, teeth often yield intact indicators even when other metrics, such as finger prints and DNA, are missing. Forensics on dental identification is now mostly manual work which is time and resource intensive. Systems for automated human identification from dental X-ray images have the potential to greatly reduce the necessary efforts spent on dental identification, but it requires a system with high stability and accuracy so that the results can be trusted.

This paper proposes a new system for automated dental X-ray identification. The scheme extracts tooth and dental work contours from the X-ray images and uses the Hausdorff-distance measure for ranking persons. This combination of state-of-the-art approaches with a novel lowest cost path-based method for separating a dental X-ray image into individual teeth, is able to achieve comparable and better results than what is available in the literature.

The proposed scheme is fully functional and is used to accurately identify people within a real dental database. The system is able to perfectly separate 88.7% of the teeth in the test set. Further, in the verification process, the system ranks the correct person in top in 86% of the cases, and among the top five in an astonishing 94% of the cases. The approach has compelling potential to significantly reduce the time spent on dental identification.

Keywords: Path-finding; Human dental identification

REFERENCES

  • [1] H. H. A. Diaa Eldin M. Nassar, “A Prototype Automated Dental Identification System (ADIS).” 2003.Google Scholar

  • [2] G. Fahmy, D. Nassar, E. Haj-Said, H. Chen, O. Nomir, J. Zhou, R. Howell, H. H. Ammar, M. Abdel-Mottaleb, and A. K. Jain, “Towards an Automated Dental Identification System (ADIS),” in Biometric Authentication, ser. Lecture Notes in Computer Science, D. Zhang and A. K. Jain, Eds. Springer Berlin Heidelberg, Jan. 2004, no. 3072, pp. 789–796.Google Scholar

  • [3] M. Abdel-Mottaleb, O. Nomir, D. Nassar, G. Fahmy, and H. Ammar, “Challenges of Developing an Automated Dental Identification System,” in 2003 IEEE 46th Midwest Symposium on Circuits and Systems, vol. 1, Dec. 2003, pp. 411–414 Vol. 1.Google Scholar

  • [4] I. A. Pretty and D. Sweet, “Forensic Dentistry: A Look at Forensic Dentistry Part 1: The Role of Teeth in the Determination of Human Identity,” British Dental Journal, vol. 190, no. 7, pp. 359–366, Apr. 2001.Google Scholar

  • [5] J. Zhou and M. Abdel-Mottaleb, “A Content-based System for Human Identification Based on Bitewing Dental X-ray Images,” Pattern Recognition, vol. 38, no. 11, pp. 2132–2142, Nov. 2005.Google Scholar

  • [6] L. Vincent, “Morphological Grayscale Reconstruction in Image Analysis: Applications and Efficient Algorithms,” IEEE Transactions on Image Processing, vol. 2, pp. 176–201, 1993.Google Scholar

  • [7] E. Said, D. Nassar, G. Fahmy, and H. Ammar, “Teeth Segmentation in Digitized Dental X-ray Films Using Mathematical Morphology,” IEEE Transactions on Information Forensics and Security, vol. 1, no. 2, pp. 178–189, Jun. 2006.Google Scholar

  • [8] M. Mahoor and M. Abdel-Mottaleb, “Automatic Classification of Teeth in Bitewing Dental Images,” vol. 5, Oct. 2004, pp. 3475–3478 Vol. 5.Google Scholar

  • [9] E. Said, G. F. Fahmy, D. Nassar, and H. Ammar, “Dental X-ray Image Segmentation,” vol. 5404, 2004, pp. 409–417. [Online]. Available: http://dx.doi.org/10.1117/12.541658Crossref

  • [10] J. Zhou and M. Abdel-Mottaleb, “Automatic Human Identification Based on Dental X-ray Images,” vol. 5404, 2004, pp. 373–380.Google Scholar

  • [11] M. H. Mahoor and M. Abdel-Mottaleb, “Classification and Numbering of Teeth in Dental Bitewing Images,” Pattern Recognition, vol. 38, no. 4, pp. 577–586, Apr. 2005.Google Scholar

  • [12] F. Aeini and F. Mahmoudi, “Classification and Numbering of Posterior Teeth in Bitewing Dental Images,” in 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), vol. 6, Aug. 2010, pp. V6–66–V6–72.Google Scholar

  • [13] Y. H. Lai and P. L. Lin, “Effective Segmentation for Dental X-Ray Images Using Texture-Based Fuzzy Inference System,” in Advanced Concepts for Intelligent Vision Systems, ser. Lecture Notes in Computer Science, J. Blanc-Talon, S. Bourennane, W. Philips, D. Popescu, and P. Scheunders, Eds. Springer Berlin Heidelberg, 2008, no. 5259.Google Scholar

  • [14] P. L. Lin, Y. H. Lai, and P. W. Huang, “An Effective Classification and Numbering System for Dental Bitewing Radiographs Using Teeth Region and Contour Information,” Pattern Recognition, vol. 43, no. 4, pp. 1380–1392, Apr. 2010.Google Scholar

  • [15] P.-L. Lin, Y.-H. Lai, and P.-W. Huang, “Dental Biometrics: Human Identification Based on Teeth and Dental Works in Bitewing Radiographs,” Pattern Recognition, vol. 45, no. 3, pp. 934–946, Mar. 2012.Google Scholar

  • [16] V. Pushparaj, U. Gurunathan, and B. Arumugam, “An Effective Dental Shape Extraction Algorithm Using Contour Information and Matching by Mahalanobis Distance,” Journal of Digital Imaging, vol. 26, no. 2, Apr. 2013.Google Scholar

  • [17] F. Shamsafar, “A New Feature Extraction Method From Dental X-ray Images for Human Identification,” in 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP), Sep. 2013, pp. 397–402.Google Scholar

  • [18] R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd, 2008.Google Scholar

  • [19] O. Nomir and M. Abdel-Mottaleb, “A System for Human Identification From X-ray Dental Radiographs,” Pattern Recognition, vol. 38, no. 8, Aug. 2005.Google Scholar

  • [20] ——, “Hierarchical Dental X-Ray Radiographs Matching,” in 2006 IEEE International Conference on Image Processing, Oct. 2006, pp. 2677–2680.Google Scholar

  • [21] ——, “Human Identification From Dental X-Ray Images Based on the Shape and Appearance of the Teeth,” IEEE Transactions on Information Forensics and Security, vol. 2, no. 2, pp. 188–197, Jun. 2007.Google Scholar

  • [22] ——, “Fusion of Matching Algorithms for Human Identification Using Dental X-Ray Radiographs,” IEEE Transactions on Information Forensics and Security, vol. 3, no. 2, pp. 223–233, Jun. 2008.Google Scholar

  • [23] ——, “Hierarchical Contour Matching for Dental X-ray Radiographs,” Pattern Recognition, vol. 41, no. 1, pp. 130–138, Jan. 2008.Google Scholar

  • [24] N. Al-sherif, G. Guo, and H. Ammar, “A New Approach to Teeth Segmentation,” in 2012 IEEE International Symposium on Multimedia (ISM), Dec. 2012, pp. 145–148.Google Scholar

  • [25] A. K. Jain, H. Chen, and S. Minut, “Dental Biometrics: Human Identification Using Dental Radiographs,” in Proc. of 4th Int’l Conf. on Audio- and Video-Based Biometric Person Authentication (AVBPA, 2003, pp. 429–437.Google Scholar

  • [26] M. Omanovic and J. J. Orchard, “Image Registration-Based Approach to Ranking Dental X-Ray Images for Human Forensic Identification,” Canadian Society of Forensic Science Journal, vol. 41, no. 3, pp. 125–134, Jan. 2008.Google Scholar

  • [27] A. K. Jain and H. Chen, “Matching of Dental X-ray Images for Human Identification,” Pattern Recognition, vol. 37, no. 7, pp. 1519–1532, Jul. 2004.Google Scholar

  • [28] B. H. Le, Z. Deng, J. Xia, Y.-B. Chang, and X. Zhou, “An Interactive Geometric Technique for Upper and Lower Teeth Segmentation,” in Medical Image Computing and Computer-Assisted Intervention MICCAI 2009, ser. Lecture Notes in Computer Science, G.-Z. Yang, D. Hawkes, D. Rueckert, A. Noble, and C. Taylor, Eds. Springer Berlin Heidelberg, 2009, no. 5762, pp. 968–975.Google Scholar

  • [29] H. Chen and A. Jain, “Dental Biometrics: Alignment and Matching of Dental Radiographs,” in Seventh IEEE Workshops on Application of Computer Vision, 2005. WACV/MOTIONS ’05 Volume 1, vol. 1, Jan. 2005, pp. 316–321.Google Scholar

  • [30] ——, “Tooth Contour Extraction for Matching Dental Radiographs,” in Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004, vol. 3, Aug. 2004, pp. 522–525 Vol.3.Google Scholar

  • [31] M. Hofer and A. Marana, “Dental Biometrics: Human Identification Based On Dental Work Information,” in XX Brazilian Symposium on Computer Graphics and Image Processing, 2007. SIBGRAPI 2007, Oct. 2007.Google Scholar

  • [32] S. Shah, A. Abaza, A. Ross, and H. Ammar, “Automatic Tooth Segmentation Using Active Contour Without Edges,” in Biometric Consortium Conference, 2006 Biometrics Symposium: Special Session on Research at the, Sep. 2006, pp. 1–6.Google Scholar

  • [33] D. E. M. Nassar and H. H. Ammar, “A Neural Network System for Matching Dental Radiographs,” Pattern Recognition, vol. 40, no. 1, pp. 65–79, Jan. 2007.Google Scholar

  • [34] P. Marquez-Neila, L. Baumela, and L. Alvarez, “A Morphological Approach to Curvature-Based Evolution of Curves and Surfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 36, no. 1, pp. 2–17, Jan. 2014.Google Scholar

About the article

Published Online: 2017-03-04

Published in Print: 2016-12-01


Citation Information: Scandinavian Journal of Forensic Science, ISSN (Online) 2353-0707, DOI: https://doi.org/10.1515/sjfs-2016-0008.

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© 2016 Danish Society of Forensic Medicine. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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