Adaptive GPU Ray Casting Based on Spectral Analysis

  • Stefan Suwelack
  • Eric Heitz
  • Roland Unterhinninghofen
  • Rüdiger Dillmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6326)


GPU based ray casting has become a valuable tool for the visualization of medical image data. While the method produces high-quality images, its main drawback is the high computational load. We present a novel adaptive approach to speed up the rendering. In contrast to well established heuristic methods, we use the spectral decomposition of the transfer function and the dataset to derive a suitable sampling criterion. It is shown how this criterion can be efficiently incorporated into an adaptive ray casting algorithm. Two medical datasets, which each represent a typical, but different material distribution, are rendered using the proposed method. An analysis of the number of sample points per ray reveals that the new algorithm requires 50% to 80% less points compared to a non-adaptive method without any quality loss. We also show that the rendering speed of the GPU implementation is greatly increased with reference to the non-adaptive algorithm.


Transfer Function Graphic Processing Unit Spectral Decomposition Volume Rendering Adaptive Sampling 
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 2010

Authors and Affiliations

  • Stefan Suwelack
    • 1
  • Eric Heitz
    • 1
  • Roland Unterhinninghofen
    • 1
  • Rüdiger Dillmann
    • 1
  1. 1.Institute of Anthropomatics (IFA) Humanoids and Intelligence Systems Laboratories (HIS)Karlsruhe Institute of Technology (KIT)Germany

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