Cellular Neural Networks and Dynamic Enhancement for Cephalometric Landmarks Detection

  • D. Giordano
  • R. Leonardi
  • F. Maiorana
  • C. Spampinato
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4029)


Cephalometric landmarks detection is a knowledge intensive activity to identify on X-rays of the skull key points to perform measurements needed for medical diagnosis and treatment. We have elsewhere proposed CNNs (Cellular Neural Networks) to achieve an accuracy in automated landmarks detection suitable for clinical practice, and have applied the method for 8 landmarks located on the bone profile. This paper proposes and evaluates a CNNs approach augmented by local image dynamic enhancemet for other 3 landmarks that are notoriously difficult to locate; the advantages of this method in the landmark detection problem are pointed out.


Recognition Rate Cellular Neural Network Active Shape Model Statistical Pattern Recognition Pulse Couple Neural Network 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Cardillo, J., Sid-Ahmed, M.A.: An image processing system for locating craniofacial landmarks. IEEE Trans. On Medical Imaging 13, 275–289 (1994)CrossRefGoogle Scholar
  2. 2.
    Hutton, T.J., Cunningham, S., Hammond, P.: An evaluation of active shape models for the automatic identification of cephalometric landmarks. European Journal of Orthodontics 22, 499–508 (2000)CrossRefGoogle Scholar
  3. 3.
    Rudolph, D.J., Sinclair, P.M., Coggins, J.M.: Automatic computerized radiograohic identification of cephalometric landmarks. American Journal of Orthodontics and Dentofacial Orthopedics 113, 173–179 (1998)CrossRefGoogle Scholar
  4. 4.
    Liu, J., Chen, Y., Cheng, K.: Accuracy of computerized automatic identification of cephalometric landmarks. American Journal of Orthodontics and Dentofacial Orthopedics 118, 535–540 (2000)CrossRefGoogle Scholar
  5. 5.
    Giordano, D., Leonardi, R., Maiorana, F., Cristaldi, G., Distefano, M.: Automatic landmarking of cephalograms by CNNS. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds.) AIME 2005. LNCS (LNAI), vol. 3581, pp. 342–352. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Levy-Mandel, A.D., Venetsamopolus, A.N., Tsosos, J.K.: Knowledge based landmarking of cephalograms. Computers and Biomedical Research 19, 282–309 (1986)CrossRefGoogle Scholar
  7. 7.
    Parthasaraty, S., Nugent, S.T., Gregson, P.G., Fay, D.F.: Automatic landmarking of cephalograms. Computers and Biomedical research 22, 248–269 (1989)CrossRefGoogle Scholar
  8. 8.
    Tong, W., Nugent, S.T., Jensen, G.M., Fay, D.F.: An algorithm for locating landmarks on dental X-Rays. In: 11th IEEE Int. Conf. on Engineering in Medicine & Biology (1990)Google Scholar
  9. 9.
    Davis, D.N., Taylor, C.J.: A blackboard architecture for automating cephalometric analysis. Journal of Medical Informatics 16, 137–149 (1991)CrossRefGoogle Scholar
  10. 10.
    Grau, V., Alcaniz, M., Juan, M.C., Monserrat, C., Knoll, C.: Automatic localization of cephalometric landmarks. Journal of Biomedical Informatics 34, 146–156 (2001)CrossRefGoogle Scholar
  11. 11.
    Chen, Y., Cheng, K., Liu, J.: Improving Cephalogram analysis through feature subimage extraction. IEEE Engineering in Medicine and Biology, 25–31 (1999)Google Scholar
  12. 12.
    El-Feghi, I., Sid-Ahmed, M.A., Ahmadi, M.: Automatic localization of craniofacial landmarks for assisted cephalometry. Pattern Recognition 34, 609–621 (2004)CrossRefGoogle Scholar
  13. 13.
    Sanei, S., Sanei, P., Zahabsaniesi: Cephalograms analysis applying template matching and fuzzy logic. Image and Vision Computing 18, 39–48 (1999)CrossRefGoogle Scholar
  14. 14.
    Innes, A., Ciesilski, V., Mamutil, J., Sabu, J.: Landmark detection for cephalometric radiology images using Pulse Coupled Neural Networks. In: Arabnia, H., Mun, Y. (eds.) Proc. Int. Conf. on Artificial Intelligence, vol. 2, CSREA Press (2002)Google Scholar
  15. 15.
    Romaniuk, B., Desvignes, M., Revenu, M., Deshayes, M.-J.: Shape variability and spatial relationships modeling in statistical pattern recognition. Pattern Recognition Letters 25, 239–247 (2004)CrossRefGoogle Scholar
  16. 16.
    El-Feghi, I., Sid-Ahmed, M.A., Ahmadi, M.: Craniofacial landmarks extraction by partial least squares regression. In: Proc. Of the 2004 International symposium on Circuits and Systems (ISCAS 2004), vol. V, pp. 45–48 (2004)Google Scholar
  17. 17.
    Chua, L.O., Roska, T.: The CNN paradigm. IEEE TCAS, I 40, 147–156 (1993)MATHMathSciNetGoogle Scholar
  18. 18.
    Szabo, T., Barsi, P., Szolgay, P.: Application of analogic CNN algorithms in telemedical neuroradiology. In: Proc. 7th IEEE International Workshop on Cellular Neural Networks and Their Applications (CNNA 2002), pp. 579–586 (2002)Google Scholar
  19. 19.
    Aizemberg, I., Aizenberg, N., Hiltner, J., Moraga, C., Meyer zu Bexten, E.: Cellular neural networks and computational intelligence in medical image processing. Image and vision computing 19, 177–183 (2001)CrossRefGoogle Scholar
  20. 20.
    Roska, T., Kek, L., Nemes, L., Zarandy, S.P.: CSL CNN Software Library. Templates and Algorithms, Budapest, Hungary (1999)Google Scholar
  21. 21.
    Liñán, G., Domnguez-Castro, R., Espejo, S., Rodríguez-Vázquez, A.: ACE16k: A Programmable Focal Plane Vision Processor with 128 x 128 Resolution. In: ECCTD 2001-European Conference on Circuit Theory and Design, Espoo, Finland, August 28-31, 2001, pp. 345–348 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • D. Giordano
    • 1
  • R. Leonardi
    • 2
  • F. Maiorana
    • 1
  • C. Spampinato
    • 1
  1. 1.Dipartimento Ingegneria Informatica e delle TelecomunicazioniUniversity of CataniaCataniaItaly
  2. 2.Policlinico Cittá UniversitariaClinica Odontoiatrica II – University of CataniaCataniaItaly

Personalised recommendations