Parallel Reflective Symmetry
Transformation for Volume Data
Parallel Reflective Symmetry
Transformation for Volume Data
Abstract
Parallel Reflective Symmetry Transformation for Volume Data
in the proceedings of Eurographics/ACM SIGGRAPH Symposium on Parallel Graphics and Visualization
Yuan Hong and Han-Wei Shen, 2007
Publication
Many volume data possess symmetric features that can be clearly observed, for example, those existing in diffusion tensor image data sets. The exploitations of symmetries for volume data sets, however, are relatively limited due to the prohibitive computational cost of detecting the symmetries. In this paper we present an efficient parallel algorithm for symmetry computation in volume data represented by regular grids. Optimization is achieved by converting the raw data into a hierarchical tree-like structure. We use a novel algorithm to partition the tree and distribute the data among processors to minimize the data dependency at run time. The computed symmetries are useful for several volume data applications, including optimal view selection and
slice position explorion.
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The Ohio State University, Department of Computer Science and Engineering
395 Dreese Lab, 2015 Neil Avenue, Columbus OH 43210
Professor Han-Wei Shen
hwshen@cse.ohio-state.edu (V) 614 292 0060 (F) 614 2922911