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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)

Abstract

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.

Keywords

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.

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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

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