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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)

Abstract

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.

Keywords

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.

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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

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