Performance analysis of accelerated image registration using GPGPU

Proceedings of 2nd Workshop on General Purpose Processing on Graphics Processing Units, GPGPU-2

Volumn , Issue , 2009, Pages 38-

  Bui, Peter ^a   Brockman, Jay ^a  

^a UNIVERSITY OF NOTRE DAME   (United States)

Author keywords

CUDA; GPGPU; Image registration; Performance analysis

Indexed keywords

BICUBIC INTERPOLATION; BILINEAR INTERPOLATION; COMPUTE UNIFIED DEVICE ARCHITECTURES; CUDA; GPGPU; GPU IMPLEMENTATION; MEMORY RESOURCES; PARALLEL PROCESSING; PEAK PERFORMANCE; PERFORMANCE ANALYSIS; PROGRAMMING ENVIRONMENT;

INTERPOLATION; PROGRAM PROCESSORS;

IMAGE REGISTRATION;

EID: 67650689005     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1513895.1513900     Document Type: Conference Paper

References (17)

1. Free Software Foundation. GNU scientic library. http://www.gnu.org/ software/gsl/.
2. J. Fung and S. Mann. Using graphics devices in reverse: GPU-based image processing and computer vision. In IEEE Int'l Conf. on Multimedia & Expo, pages 9-12, 2008.
3. A. A. Goshtasby. 2-D and 3-D Image Registration. Wiley-Interscience, 2005.
4. M. Harris. Mapping computational concepts to GPUs. In GPU Gems 2, pages 493{508. Addison Wesley, 2005.
5. K. E. Hillesland and A. Lastra. GPU oating-point paranoia. In GP2 ACM Workshop on General Purpose Computing on Graphics Processors, page 8, 2004.
6. F. Ino, J. Gomita, Y. Kawasaki, and K. Hagihara. A GPGPU approach for accelerating 2-d/3-d rigid registration of medical images. In International Symposium on Parallel and Distributed Processing and Applications (ISPA), pages 939-950, 2006.
7. A. Kubias, F. Deinzer, T. Feldmann, D. Paulus, B. Schreiber, and T. Brunner. 2d/3d image registration on the GPU. Pattern Recognition and Image Analysis, 18(3):381-389, 2008.
8. P. Muyan-Özçelik,J. D. Owens, J. Xia, and S. S. Samant. Fast deformable registration on the GPU: A CUDA implementation of demons. In International Conference on Computational Science and Its Applications (ICCSA), pages 223-233, 2008.
9. NVIDIA. NVIDIA CUDA Compute Unified Device Architecture, Programming Guide, Version 2.0. NVIDIA, 2008.
10. A. Obukhov and A. Kharlamov. Dct8x8. NVIDIA Software Development Kit (SDK), 2008.
11. J. D. Owens, M. Houston, D. Luebke, S. Green, J. E. Stone, and J. C. Phillips. GPU computing. Proceedings of the IEEE, 96(5):879-899, 2008.
12. W. Plishker, O. Dandekar, S. Bhattacharyya, and R. Shekhar. Towards a heterogeneous medical image registration acceleration platform. Biomedical Circuits and Systems Conference (BIOCAS), pages 231-234, Nov. 2007.
13. C. Sigg and M. Hadwiger. Fast third-order texture filtering. In GPU Gems 2, pages 313-317. Addison Wesley, 2005.
14. T. Sugiura, D. Deguichi, T. Kitasaka, K. Mori, and Y. Suenaga. A method for accelerating bronchoscope tracking based on image registration by GPGPU. In Augmented environments for Medical Imaging including Augmented Reality in Computer-aided Surgery (AMI-ARCS) 2008 (Medical Image Computing and Computer Assisted Intervention 2008), 2008.
15. P. Thévenaz, U. Ruttimann, and M. Unser. A pyramid approach to subpixel registration based on intensity. IEEE Transactions on Image Processing, 7(1):27-41, January 1998. (Pubitemid 128745320)
16. V. Volkov and J. W. Demmel. Benchmarking GPUs to tune dense linear algebra. In SC '08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing, pages 1-11, Piscataway, NJ, USA, 2008. IEEE Press.
17. B. Zitová and J. Flusser. Image registration methods: a survey. Image and Vision Computing, 21(11):977-1000, October 2003.