High performance noise reduction for biomedical multidimensional data

Abstract

Anisotropic nonlinear diffusion (AND) is one of the most powerful noise reduction techniques in the field of image processing. This method is based on a partial differential equation (PDE) tightly coupled with a massive set of eigensystems. Denoising large 3D images in biomedicine and structural cellular biology by AND is extremely expensive from a computational point of view, with huge memory needs. In this work, high performance computing techniques have been applied to AND. An strategy for optimal memory usage has been designed, which has allowed a remarkable reduction of the memory requirements. Parallel implementations of AND have been developed with special focus on clusters of symmetric multiprocessors (SMPs), currently a dominant platform in high performance computing. Different programming models have been used for the parallelization of AND: (1) Message-passing paradigm using MPI and (2) a hybrid paradigm that uses message passing among nodes plus the shared address space paradigm between the processors within the nodes. The parallel approaches have been evaluated on a cluster of dual-Xeon nodes, a representative example of clusters of SMPs. The conclusion drawn is that the hybrid approach is the most suitable for the parallelization of AND for this kind of computing platforms.