"Fast Parallel Image Registration on CPU and GPU for Diagnostic Classification of Alzheimer's Disease"

Denis P Shamonin, Esther E Bron, Boudewijn P.F. Lelieveldt, Marion Smits, Stefan Klein and Marius Staring


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 accelerated registration tool is employed in a study on diagnostic classification of Alzheimer's disease and cognitively normal controls based on T1-weighted MRI. We selected 299 participants from the publicly available Alzheimer's Disease Neuroimaging Initiative database. Classification is performed with a support vector machine based on gray matter volumes as a marker for atrophy. We evaluated two types of strategies (voxel-wise and region-wise) that heavily rely on nonrigid image registration.

Parallelization and optimization resulted in an acceleration factor of 4-5x on an 8-core machine. Using OpenCL a speedup factor of ~2 was realized for computation of the Gaussian pyramids, and 15-60 for the resampling step, for larger images. The voxel-wise and the region-wise classification methods had an area under the receiver operator characteristic curve of 88% and 90%, respectively, both for standard and accelerated registration.

We conclude that the image registration package elastix was substantially accelerated, with nearly identical results to the non-optimized version. The new functionality will become available in the next release of elastix as open source under the BSD license.



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Copyright © 2014 by the authors. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.


Source code

The source code of the methods described in this paper can be found in the image registration toolkit elastix, available at https://github.com/SuperElastix/elastix.

BibTeX entry

author = {Shamonin, Denis P and Bron, Esther E and Lelieveldt, Boudewijn P.F. and Smits, Marion and Klein, Stefan and Staring, Marius},
title = {Fast Parallel Image Registration on CPU and GPU for Diagnostic Classification of Alzheimer's Disease},
journal = {Frontiers in Neuroinformatics},
volume = {7},
number = {50},
pages = {1-15},
month = {January},
year = {2014},

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