Real-Time Denoising of Medical X-Ray Image Sequences: Three Entirely Different Approaches

  • Marc Hensel
  • Thomas Pralow
  • Rolf-Rainer Grigat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


Low-dose X-ray image sequences exhibit severe signal-dependent noise that must be reduced in real-time while, at the same time, preserving diagnostic structures and avoiding artifacts. We propose three different methods with applications beyond medical image processing. Major contributions are innovative motion detection based on independent binarization of positive and negative temporal differences, real-time multiscale nonlinear diffusion in the presence of severe signal-dependent noise, and multi-resolution inter-scale correlation in shift-dependent pyramids. All methods exhibit excellent performance over a broad range of noise, detail, and contrast levels. As performance in medical imaging depends to a large degree on the type of intervention and individual preferences of medical staff, no method is generally superior and all methods are considered for the next generation of fluoroscopy systems.


Noise Reduction Motion Detection Impulse Noise High Noise Level Strong Noise 
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.
    Aufrichtig, R., Wilson, D.L.: X-Ray Fluoroscopy Spatio-Temporal Filtering with Object Detection. IEEE Trans. Medical Imaging 14, 733–746 (1995)CrossRefGoogle Scholar
  2. 2.
    Bao, P., Zhang, L.: Noise Reduction for Multiscale Resonance Images via Adaptive Multiscale Products Thresholding. IEEE Trans. Medical Imaging 22, 1089–1099 (2003)CrossRefGoogle Scholar
  3. 3.
    Burt, P.J., Adelson, E.H.: The Laplacian Pyramid as a Compact Image Code. IEEE Trans. Communications COM-31, 532–540 (1983)CrossRefGoogle Scholar
  4. 4.
    Dippel, S., Stahl, M., Wiemker, R., Blaffert, T.: Multiscale Contrast Enhancement for Radiographies: Laplacian Pyramid Versus Fast Wavelet Transform. IEEE Trans. Medical Imaging 21, 343–353 (2002)CrossRefGoogle Scholar
  5. 5.
    Donoho, D.L., Johnstone, I.M.: Ideal Spatial Adaptation by Wavelet Shrinkage. Biometrika 81, 425–455 (1994)MATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Hensel, M., Brummund, U., Pralow, T., Grigat, R.-R.: Pyramid Multiscale Products for Noise Reduction of Medical X-Ray Image Sequences. Biomed. Tech./Biomed. Eng. 50-1, 1108–1109 (2005)Google Scholar
  7. 7.
    Hensel, M., Lundt, B., Pralow, T., Grigat, R.-R.: Robust and Fast Estimation of Signal-Dependent Noise in Medical X-Ray Image Sequences. In: Handels, H., et al. (eds.) Bildverarbeitung für die Medizin 2006: Algorithmen, Systeme, Anwendungen, pp. 46–50. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Hensel, M., Wiesner, G., Kuhrmann, B., Pralow, T., Grigat, R.-R.: Motion and Noise Detection for Spatio-Temporal Filtering of Medical X-Ray Image Sequences. Biomed. Tech./Biomed. Eng. 50-1, 1106–1107 (2005)Google Scholar
  9. 9.
    Konrad, J.: Motion Detection and Estimation. In: Handbook of Image and Video Processing, 2nd edn., pp. 253–274. Elsevier Academic Press, Amsterdam (2005)CrossRefGoogle Scholar
  10. 10.
    Kunz, D., Eck, K., Fillbrandt, H., Aach, T.: A Nonlinear Multi-Resolution Gradient Adaptive Filter for Medical Images. In: SPIE Medical Imaging, SPIE, vol. 5032, pp. 732–742 (2003)Google Scholar
  11. 11.
    Lagendijk, R.L., Roosmalen, P.M.B., van Biemond, J., Rareş, A., Reinders, M.J.T.: Video Enhancement and Restoration. In: Bovik, A.C. (ed.) Handbook of Image and Video Processing, 2nd edn., pp. 275–295. Elsevier Academic Press, Amsterdam (2005)CrossRefGoogle Scholar
  12. 12.
    Mallat, S.: A Wavelet Tour of Signal Processing, 2nd edn. Academic Press, London (1999)MATHGoogle Scholar
  13. 13.
    Perona, P., Malik, J.: Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans. Pattern Analysis and Machine Intelligence 12, 629–639 (1990)CrossRefGoogle Scholar
  14. 14.
    Vuylsteke, P., Schoeters, E.: Image Processing in Computer Radiography. In: Proc. CT for Industr. Applications and Image Proc. in Radiology, pp. 87–101 (1999)Google Scholar
  15. 15.
    Weickert, J.: Anisotropic Diffusion in Image Processing. Teubner (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marc Hensel
    • 1
  • Thomas Pralow
    • 2
  • Rolf-Rainer Grigat
    • 1
  1. 1.Hamburg University of Technology, Vision SystemsHamburgGermany
  2. 2.Philips Medical Systems, General X-RayHamburgGermany

Personalised recommendations