Application of the Point Distance Histogram to the Automatic Identification of People by Means of Digital Dental Radiographic Images

  • Dariusz FrejlichowskiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9972)


In this paper, an approach for the identification of people based on digital orthopantomogram images is proposed and experimentally investigated. This approach is composed of four main stages. In the first stage, the image quality is enhanced using the Laplacian pyramid. In the second stage, the image is segmented into individual sub-images, each containing a single tooth. To do this, the line that separates the upper and lower jaw is obtained using integral projections, and then information about the intensity and location of particular types of tooth is applied. The extraction of the shapes of the teeth is the third stage. This stage also later involves each particular shape being represented using the Point Distance Histogram algorithm to obtain its description. Finally, the resultant descriptions are matched with the objects stored in a template base for a person and, using these, biometric identification is performed.


Active Contour Model Template Image Active Shape Model Laplacian Pyramid Tooth Shape 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The author of this paper wishes to thank gratefully MSc R. Wanat for his significant help in developing and exploring the described approach.


  1. 1.
    Bowers, M.C.: Forensic Dental Evidence. Elsevier, Boston (2004)Google Scholar
  2. 2.
    Lee, S., et al.: The diversity of dental patterns in Orthopantomography and its significance in human identification. J. Forensic Sci. 49(4), 784–786 (2004)CrossRefGoogle Scholar
  3. 3.
    Nassar, D., Ammar, H.H.: A prototype automated dental identification system (ADIS). In: Proceedings of the 2003 Annual National Conference on Digital Government Research, pp. 1–4 (2003)Google Scholar
  4. 4.
    Abdel-Mottaleb, M., et al.: Challenges of developing an automated dental identification system. In: IEEE Mid-west Symposium for Circuits and Systems, Cairo, Egypt, pp. 411–414 (2003)Google Scholar
  5. 5.
    Fahmy, G., et al.: Towards an automated dental identification system (ADIS). In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 789–796. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-25948-0_107 CrossRefGoogle Scholar
  6. 6.
    Lu, J., Healy Jr., D.M.: Contrast enhancement of medical images using multiscale edge representation. In: Proceedings of SPIE: Wavelet applications, Orlando, 5–8 April 1994Google Scholar
  7. 7.
    Dippel, S., Stahl, M., Wiemker, R., Blaffert, T.: Multiscale contrast ehnahncement for radiographies: laplacian pyramid versus fast wavelet transform. IEEE Trans. Med. Imaging 21(4), 343–353 (2002)CrossRefGoogle Scholar
  8. 8.
    Zhou, J., Abdel-Mottaleb, M.: A content-based system for human identification based on bitewing dental X-ray images. Pattern Recogn. 38(11), 2132–2142 (2005)CrossRefGoogle Scholar
  9. 9.
    Frejlichowski, D., Wanat, R.: Application of the laplacian pyramid decomposition to the enhancement of digital dental radiographic images for the automatic person identification. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6112, pp. 151–160. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-13775-4_16 CrossRefGoogle Scholar
  10. 10.
    Jain, A.K., Chen, H.: Matching of dental X-ray images for human identification. Pattern Recogn. 37(7), 1519–1532 (2004)CrossRefGoogle Scholar
  11. 11.
    Frejlichowski, D., Wanat, R.: Automatic segmentation of digital orthopantomograms for forensic human identification. In: Maino, G., Foresti, G.L. (eds.) ICIAP 2011. LNCS, vol. 6979, pp. 294–302. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24088-1_31 CrossRefGoogle Scholar
  12. 12.
    Chen, H., Jain, A.: Automatic forensic dental identification. In: Jain, A.K., Flynn, P., Ross, A.A. (eds.) Handbook of Biometrics, pp. 231–251. Springer, USA (2008)CrossRefGoogle Scholar
  13. 13.
    Frejlichowski, D., Wanat, R.: Extraction of teeth shapes from orthopantomograms for forensic human identification. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds.) CAIP 2011. LNCS, vol. 6855, pp. 65–72. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23678-5_6 CrossRefGoogle Scholar
  14. 14.
    Burt, P.J., Adelson, E.H.: The laplacian pyramid as a compact image code. IEEE Trans. Commun. 31(4), 532–540 (1983)CrossRefGoogle Scholar
  15. 15.
    Vuylsteke, P., Schoeters, E.: Image processing in computed radiography. In: Proceedings of International Symposium on Computerized Tomography for Industrial Applications and Image Processing in Radiology, Berlin, Germany, 15–17 March 1999Google Scholar
  16. 16.
    Stahl, M., Aach, T., Buzug, T.M., Dippel, S., Neitzel, U.: Noise-resistant weak-structure enhancement for digital radiography. SPIE 1999(3661), 1406–1417 (1999)Google Scholar
  17. 17.
    Frejlichowski, D.: The point distance histogram for analysis of erythrocyte shapes. Pol. J. Environ. Stud. 16(5B), 261–264 (2007)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Faculty of Computer Science and Information TechnologyWest Pomeranian University of TechnologySzczecinPoland

Personalised recommendations