An OpenCL implementation of the Gaussian pyramid and the resampler
| Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3393 |
Published in The Insight Journal - 2012 January-December.
Submitted by Denis Shamonin on 12-18-2012.
Nonrigid image registration is an important, but resource demanding and time-consuming task in medical image analysis. This limits its application in time-critical clinical routines. In this report we explore acceleration of two time-consuming parts of a registration algorithm by means of parallel processing using the GPU. We built upon the OpenCL-based GPU image processing framework of the recent ITK4 release, and implemented Gaussian multi-resolution strategies and a general resampling framework. We evaluated the performance gain on two multi-core machines with NVidia GPUs, and compared to an existing ITK4 CPU implementation. A speedup factor of ~2-4 was realized for the multi-resolution strategies and a speedup factor of ~10-46 was achieved for resampling, for larger images (~10^8 voxels).
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ITK4OpenCL-data.zip (3Mb)
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Automatic Testing Results
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Automatic Testing Results
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| Categories: | Image pyramids, Resampling, Transforms |
| Keywords: | image registration, parallelization, GPU, OpenCL, GPUResampleImageFilter, GenericMultiResolutionPyramidImageFilter, elastix |
| Tracking Number: | NWO NRG-2010.02, NWO 639.021.124 |
| Toolkits: | ITK, CMake |
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1. The source code has been merged with latest elastix see https://github.com/SuperElastix/elastix
2. The source code has been tested with latest ITK
ITK 4.12.2 (25 Oct 2017)
CUDA/v8.0 (NVidia driver 378.49)
Intel OpenCL SDK (version 7.0.0.2519) on CPU
3. The source code builds and tests performed using elastix dashboard:
http://my.cdash.org/index.php?project=elastix
4. The full paper:
http://journal.frontiersin.org/article/10.3389/fninf.2013.00050/full
5. The latest GPU performance results could be found in section supplemental data of the full paper:
http://journal.frontiersin.org/File/DownloadFile/65120/octet-stream/Presentation%201.PDF/12/1/66699
6. The recent GPU/CPU performance experiments could be found in directory test_logs see source code.