Using the Adaptive HistoPyramid to Enhance Performance of Surface Extraction in 3D Medical Image Visualisation

Abstract

There are currently a range of different approaches for extracting iso-surfaces from volumetric medical image data. Of these, the HistoPyramid appears to be one of the more promising options. This is due to its use of stream compaction and expansion which facilitates extremely efficient traversal of the HistoPyramid structure. This paper introduces a novel extension to the HistoPyramid concept that entails incorporating a variable reduction between the HP layers in order to better fit volumes with arbitrary dimensions, thus saving memory and improving performance. As with the existing HistoPyramid techniques, the adaptive version lends itself to implementation on the GPU which in turn leads to further performance improvements. Ultimately, when compared against the best performing existing HistoPyramids, the adaptive approach yielded a performance improvement of up to 20 percent without any impact on the accuracy of the extracted mesh.