An Approach for the Automatic Cephalometric Landmark Detection Using Mathematical Morphology and Active Appearance Models

  • Sylvia Rueda
  • Mariano Alcañiz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


Cephalometric analysis of lateral radiographs of the head is an important diagnosis tool in orthodontics. Based on manually locating specific landmarks, it is a tedious, time-consuming and error prone task. In this paper, we propose an automated system based on the use of Active Appearance Models (AAMs). Special attention has been paid to clinical validation of our method since previous work in this field used few images, was tested in the training set and/or did not take into account the variability of the images. In this research, a top-hat transformation was used to correct the intensity inhomogeneity of the radiographs generating a consistent training set that overcomes the above described drawbacks. The AAM was trained using 96 hand-annotated images and tested with a leave-one-out scheme obtaining an average accuracy of 2.48mm. Results show that AAM combined with mathematical morphology is the suitable method for clinical cephalometric applications.


Mathematical Morphology Active Appearance Model Intensity Inhomogeneity Cephalometric Analysis Error Prone Task 
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  1. 1.
    Rakosi, T.: An Atlas and Manual of Cephalometric Radiology. London Wolfe Medical Publications (1982)Google Scholar
  2. 2.
    Dean, D., Palomo, M., Subramanya, K., et al.: Accuracy and precision of 3D cephalometric landmarks from biorthogonal plain-film x rays. SPIE Med. Imag. 3335, 50–58 (1998)CrossRefGoogle Scholar
  3. 3.
    Geelem, W., Wenzel, A., Gotfredsen, E., Kruger, M., Hansson, L.G.: Reproducibility of cephalometric landmarks in conventional film, and hardcopy and monitor-displayed images obtained by the storage phosphor technique. Eur. J. Orthod. 20, 331–340 (1998)CrossRefGoogle Scholar
  4. 4.
    Lévy-Mandel, A., Venetsanopoulos, A., Tsotsos, J.: Knowledge-based landmarking of cephalograms. Comput. Biomed. Res. 19, 282–309 (1986)CrossRefGoogle Scholar
  5. 5.
    Parthasaraty, S., Nugent, S., Gregson, P., et al.: Automatic landmarking of cephalograms. Comput. Biomed. Res. 22, 248–269 (1989)CrossRefGoogle Scholar
  6. 6.
    Cardillo, J., Sid-Ahmed, M.A.: An image processing system for locating craniofacial landmarks. IEEE Trans. Med. Imag. 13, 275–289 (1994)CrossRefGoogle Scholar
  7. 7.
    Grau, V., Alcañíz, M., Juan, M.C., Monserrat, C., Knoll, C.: Automatic Localization of Cephalometric Landmarks. J. biomed. inform. 34, 146–156 (2001)CrossRefGoogle Scholar
  8. 8.
    Chen, Y.T., Cheng, K.S., Liu, J.K.: Improving Cephalogram Analysis through Feature Subimages Extraction. IEEE Eng. in Med. Biol. Mag. 18, 25–31 (1999)CrossRefGoogle Scholar
  9. 9.
    Hutton, T.J., Cunningham, S., Hammond, P.: An Evaluation of Active Shape Models for the Automatic Identification of Cephalometric Landmarks. Eur. J. Orthod. 22, 499–508 (2000)CrossRefGoogle Scholar
  10. 10.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active Appearance Models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  11. 11.
    Cootes, T.F., Taylor, C.J.: Statistical Models of Appearance for Computer Vision, Report (2004)Google Scholar
  12. 12.
    Cootes, T.F., Taylor, C.J.: Statistical models of appearance for medical image analysis and computer vision. In: Proc. SPIE Med. Imag. (2001)Google Scholar
  13. 13.
    Sonka, M., Fitzpatrick, J.M.: Handbook of Medical Imaging. In: Medical Image Processing and Analysis, vol. 2. SPIE Press (2000)Google Scholar
  14. 14.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision, 2nd edn. Brooks/Cole Publishing Company (1999)Google Scholar
  15. 15.
    Dryden, I.L., Mardia, K.V.: Statistical Shape Analysis. Wiley, Chichester (1998)zbMATHGoogle Scholar
  16. 16.
    Stegmann, M.B.: Active Appearance Models - Theory, Extensions & Cases, Master Thesis IMM-EKS-2000-25 (2000)Google Scholar
  17. 17.
    Stegmann, M.B., Ersboll, B.K., Larsen, R.: FAME - a flexible appearance modelling environment. IEEE Trans. Med. Imag. (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sylvia Rueda
    • 1
  • Mariano Alcañiz
    • 1
  1. 1.Medical Image Computing Laboratory (MedICLab)Universidad Politécnica de Valencia, UPV/ETSIAValenciaSpain

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