Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease.

ABSTRACT: Nonrigid image registration is an important, but time-consuming task in medical image analysis. In typical neuroimaging studies, multiple image registrations are performed, i.e., for atlas-based segmentation or template construction. Faster image registration routines would therefore be beneficial. In this paper we explore acceleration of the image registration package elastix by a combination of several techniques: (i) parallelization on the CPU, to speed up the cost function derivative calculation; (ii) parallelization on the GPU building on and extending the OpenCL framework from ITKv4, to speed up the Gaussian pyramid computation and the image resampling step; (iii) exploitation of certain properties of the B-spline transformation model; (iv) further software optimizations. The acceler

SUBMITTER: Shamonin DP 

PROVIDER: S-EPMC3893567 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature