Blind Background Subtraction in Dental Panoramic X-Ray Images: An Application Approach

  • Peter Michael Goebel
  • Nabil Ahmed Belbachir
  • Michael Truppe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3663)


Dental Panoramic X-ray images are images having complex content, because several layers of tissue, bone, fat, etc. are superimposed. Non-uniform illumination, stemming from the X-ray source, gives extra modulation to the image, which causes spatially varying X-ray photon density. The interaction of the X-ray photons with the density of matter causes spatially coherent varying noise contribution. Many algorithms exist to compensate background effects, by pixel based or global methods. However, if the image is contaminated by a non-negligible amount of noise, that is usually non-Gaussian, the methods cannot approximate the background efficiently. In this paper, a dedicated approach for background subtraction is presented, which operates blind, that means the separation of a set of independent signals from a set of mixed signals, with at least, only little a priori information about the nature of the signals, using the A-Trous multiresolution transform to alleviate this problem. The new method estimates the background bias from a reference scan, which is taken without a patient. The background values are rescaled by a polynomial compensation factor, given by mean square error criteria, thus subtracting the background will not produce additional artifacts in the image. The energy of the background estimate is subtracted from the energy of the mixture. The method is capable to remove spatially varying noise also, allocating an appropriate spatially noise estimate. This approach has been tested on 50 images from a database of panoramic X-ray images, where the results are cross validated by medical experts.


Background Subtraction Method Photoelectric Absorption Poisson Count Background Mixture Dental Panoramic Radiography 
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.
    Aird EGA. Basic Physics for Medical Imaging. Heinemann, Oxford (1988)Google Scholar
  2. 2.
    Barten, P.G.J.: Physical model for the Contrast Sensitivity of the human eye. In: Proc. SPIE, vol. 1666, pp. 57–72 (1992)Google Scholar
  3. 3.
    Donoho, D.L.: Denoising by Soft-thresholding. IEEE Trans. on Information Theory 41, 613–627 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Goebel, P.M., Belbachir, A.N., Truppe, M.: Noise estimation in panoramic X-ray images: An application analysis approach. In: Workshop on Statist. Signal Proc., SSP 2005, Bordeaux, July 17-20 (2005)Google Scholar
  5. 5.
    Goebel, P.M., Belbachir, A.N.: Background Removal in Dental Panoramic X-ray Images by the A-Trous Multires. Trans. To appear at ECCTD 2005, Cork, IE (2005)Google Scholar
  6. 6.
    Gutchessy, D., Trajkovicz, M., Cohen-Solalz, E., Lyonsz, D., Jain, A.K.: A Background Model Initialization Algorithm for Video Surveillance. In: Proc. International Conference on Computer Vision, Vancouver, Canada (July 2001)Google Scholar
  7. 7.
    Holschneider, M., Kronland-Martinet, R., Morlet, J., Tchamitchian, P.: A real-time algorithm for signal analysis with help of the wavelet transform. In: Combes, J.M., Grossmann, A., Tchamitchian, P. (eds.) Wavelets, Time-Frequency Methods and Phase Space, pp. 286–297. Springer, Berlin (1989)Google Scholar
  8. 8.
    Hubbell, J.H., Seltzer, S.M.: Tables of X-Ray Mass Attenuation Coefficients and Mass Energy-Absorption Coefficients (version 1.4). NISTIR 5632, National Institute of Standards and Technology, Gaithersburg, MD (1995)Google Scholar
  9. 9.
    Srihari, S.N., Leedham, G.: A survey of computer methods in forensic document examination. In: Proc. Int. Graphonomics Soc. Conf., Scottsdale, USA, November 2-5 (2003)Google Scholar
  10. 10.
    Long, W., Yang, Y.-H.: Stationary background generation: an alternative to the difference of two images. Pattern Recogn. 23(12), 1351–1359 (1990)CrossRefGoogle Scholar
  11. 11.
    Ridder, C., Munkelt, O., Kirchner, H.: Adaptive Background Estimation and Foreground Detection using Kalman-Filtering. In: Proc. Int. Conf. on recent Advances in Mechatronics ICRAM 1995, UNESCO Chair on Mechatronics, pp. 193–199 (1995)Google Scholar
  12. 12.
    Savakis, A.E.: Adaptive document image thresholding using foreground and background clustering. In: 1998 Int. Conf. on Image Processing (ICIP 1998), October 4-7 (1998)Google Scholar
  13. 13.
    Shelley, A.J., Seed, N.L.: Approaches to Static Background Identification and Removal. Journal of the Institution of Electrical Engineers (1993)Google Scholar
  14. 14.
    Shensha, M.J.: Discrete Inverses for Nonorthogonal Wavelet Transforms. IEEE Trans. on Signal Processing 44(4), 798–807 (1996)CrossRefGoogle Scholar
  15. 15.
    Su, T.M., Hu, J.S.: Background Removal in Vision Servo System Using Gaussian Mixture Model Framework. In: IEEE Proc. on Int. Conf. on Networking, Sensing and Control, Taipei, TW (2004)Google Scholar
  16. 16.
    Thevenaz, P., Unser, M.: A Pyramid Approach to Subpixel Registration Based on Intensity. IEEE Trans. on Image Processing 7(1), 27–41 (1998)CrossRefGoogle Scholar
  17. 17.
    Zhang, J., Huang, H.K.: Automatic Background Recognition and Removal (ABRR) in Computed Radiography Images. IEEE Trans. on Medical Imaging 16(6) (December 1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Peter Michael Goebel
    • 1
    • 2
  • Nabil Ahmed Belbachir
    • 1
  • Michael Truppe
    • 3
  1. 1.Institute of Computer Aided Automation, Pattern Recognition and Image Processing GroupVienna University of TechnologyAustria
  2. 2.Technical Project- and ProcessmanagementAustria
  3. 3.Karl Landsteiner Institute for BiotelematicsDanube University KremsAustria

Personalised recommendations