Parallel reflective symmetry transformation for volume data

Many volume data possess symmetric features that can be clearly observed, for example, those existing in diffusion tensor images. The exploitations of symmetries for volume datasets, 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 visualization applications, including optimal view selection and slice position exploration.