Adaptive GPU Ray Casting Based on Spectral Analysis

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

Abstract

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.

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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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