GPU-based segmentation of cervical vertebra in X-Ray images

[en] The segmentation of cervical vertebra in X-Ray radiographs can give valuable information for the study of the vertebral mobility. One particular characteristic of the X-Ray images is that they present very low grey level variation and makes the segmentation difficult to perform. In this paper, we propose a segmentation procedure based on the Active Shape Model to deal with this issue. However, this application is seriously hampered by its considerable computation time. We present how vertebra extraction can efficiently be performed in exploiting the vast processing power of the Graphics Processing Units (GPU). We propose a CUDA-based GPU implementation of the most intensive processing steps enabling to boost performance. Experimentations have been conducted using a set of high resolution X-Ray medical images, showing a global speedup ranging from 15 to 21, by comparison with the CPU implementation.

Research center :

BIOSYS - Biosys

Disciplines :

Computer science

Author, co-author :

Mahmoudi, Sidi ; Université de Mons > Faculté Polytechnique > Informatique, Logiciel et Intelligence artificielle

Lecron, Fabian ; Université de Mons > Faculté Polytechnique > Informatique, Logiciel et Intelligence artificielle

Manneback, Pierre ; Université de Mons > Faculté Polytechnique > Informatique, Logiciel et Intelligence artificielle

Benjelloun, Mohammed ; Université de Mons > Faculté Polytechnique > Informatique, Logiciel et Intelligence artificielle

Mahmoudi, Said ; Université de Mons > Faculté Polytechnique > Informatique, Logiciel et Intelligence artificielle

Language :

English

Title :

GPU-based segmentation of cervical vertebra in X-Ray images

Publication date :

20 September 2010

Event name :

IEEE International Conference on Cluster Computing

Event place :

Crete, Greece

Event date :

2010

Research unit :

F114 - Informatique, Logiciel et Intelligence artificielle

Research institute :

R450 - Institut NUMEDIART pour les Technologies des Arts Numériques

Available on ORBi UMONS :

since 18 January 2011

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Bibliography

M. Benjelloun, S. Mahmoudi, and F. Lecron, "A New Semi-Automatic Approach For X-Ray Cervical Images Segmentation Using Active Shape Model," in Proceedings of the 3rd International Conference on Bioinspired Systems and Signal Processing, 2010, pp. 501-506.
F. Lecron, M. Benjelloun, and S. Mahmoudi, "Points of interest detection in cervical spine radiographs by polygonal approximation," in Proceedings of the 2nd International Conference on Image Processing Theory, Tools and Applications, 2010, pp. 81-86.
R. Deriche, "Using Canny's criteria to derive a recursively implemented optimal edge detector," Internat. J. Vision,Boston, pp. 167-187, 1987.
S.-H. Huang, S.-H. Lai, and C. L. Novak, "A statistical learning appproach to vertebra detection and segmentation from spinal MRI," in 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008, pp. 125-128.
S.-H. Huang, Y.-H. Chu, S.-H. Lai, and C. L. Novak, "Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI." IEEE Transactions on Medical Imaging, vol. 28, no. 10, pp. 1595-1605, 2009.
S.-F. Wong, K.-Y. K. Wong, W.-N. K. Wong, C.-Y. J. Leong, and D.- K. K. Luk, "Tracking Lumbar Vertebrae in Digital Videofluoroscopic Video Automatically," in Medical Imaging and Augmented Reality, 2004, pp. 154-162.
A. Wong, A. Mishra, P. Fieguth, D. Clausi, N. M. Dunk, and J. P. Callaghan, "Shape-guided active contour based segmentation and tracking of lumbar vertebrae in video fluoroscopy using complex wavelets." Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2008, pp. 863-866, 2008.
S. Tan, J. Yao, M. M. Ward, L. Yao, and R. M. Summers, "Level set based vertebra segmentation for the evaluation of Ankylosing Spondylitis," Proceedings of the SPIE. Medical Imaging 2006: Image Processing, vol. 6144, pp. 58-67, 2006.
G. Zamora, H. Sari-Sarraf, and L. R. Long, "Hierarchical segmentation of vertebrae from x-ray images," in Medical Imaging 2003: Image Processing, M. Sonka and J. M. Fitzpatrick, Eds., vol. 5032, no. 1. San Diego, CA, USA: SPIE, 2003, pp. 631-642.
S. Mahmoudi and M. Benjelloun, "A New Approach for Cervical Vertebrae Segmentation," Progress in Pattern Recognition, Image Analysis and Applications, vol. 4756, pp. 753-762, 2008.
T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, "Active shape models-their training and application," Computer vision and image understanding, vol. 61, no. 1, pp. 38-59, 1995.
T. F. Cootes, G. J. Edwards, and C. J. Taylor, "Active Appearance Models," in 5th European Conference on Computer Vision. Springer-Verlag, 1998, pp. 484-498.
Z. Yang, Y. Zhu, and Y. pu, "Parallel Image Processing Based on CUDA," International Conference on Computer Science and Software Engineering. China, pp. 198-201, 2008.
J. Fung, S. Mann, and C. Aimone, "OpenVIDIA:Parallel gpu computer vision." In Proc of ACM Multimedia, pp. 849-852, 2005.
OpenGL, "OpenGL Architecture Review Board: ARB vertex program. Revision 45." 2004. [Online]. Available: http://oss.sgi.com/projects/ogl- sample/registry/
W. R. Mark, R. S. Glanville, K. Akeley, and M. J. Kilgard, "Cg: A system for programming graphics hardware in a C-like language," ACM Transactions on Graphics 22, pp. 896-907, 2003.
NVIDIA, "NVIDIA CUDA," 2007. [Online]. Available: http://www.nvidia.com/cuda.
Y. Luo and R. Duraiswani, "Canny Edge Detection on NVIDIA CUDA," Proceedings of the Workshop on Computer Vision on GPUS, CVPR, 2008.
J. Canny, "A computational approach to edge detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679-714, 1986.
Y. Heng and L. Gu, "GPU-based Volume Rendering for Medical Image Visualization," Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, pp. 5145-5148, 2005.
M. Smelyanskiy, D. Holmes, J. Chhugani, A. Larson, and al, "Mapping high-fidelity volume rendering for medical imaging to CPU, GPU and many-core architectures," IEEE Transactions on Visualization and Computer Graphics, 15(6), pp. 1563-1570, 2009.
T. Schiwietz, T. Chang, P. Speier, and R. Westermann, "MR image reconstruction using the GPU," Image-Guided Procedures, and Display. Proceedings of the SPIE, pp. 646-655, 2006.
T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, "Training models of shape from sets of examples," in British Machine Vision Conference, vol. 557, 1992, pp. 266-275.
S. M. Pizer, E. P. Amburn, J. D. Austin, R. Cromartie, A. Geselowitz, T. Greer, B. Ter Haar Romeny, J. B. Zimmerman, and K. Zuiderverld, "Adaptive histogram equalization and its variations," Computer Vision, Graphics, and Image Processing, vol. 39, no. 3, pp. 355-368, 1987.
D. H. Douglas and T. K. Peucker, "Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature," Cartographica: The International Journal for Geographic Information and Geovisualization, vol. 10, no. 2, pp. 112-122, 1973.
U. Ramer, "An iterative procedure for the polygonal approximation of plane curves," Computer Graphics and Image Processing, vol. 1, pp. 244-256, 1972.
GPU4VISION, "GPU4VISION," 2010. [Online]. Available: http: //www.gpu4vision.org
J. D. Owens, D. Luebke, N. Govindaraju, M. Harris, J. Kruger, A. E. Lefohn, and T. J. Purcell, "A survey of general-purpose computation on graphics hardware," Computer Graphics Forum, 26(1), pp. 80-113, 2007.
NVIDIA, "NVIDIA CUDA SDK code samples." [Online]. Available: http://developer.download.nvidia.com/compute/cuda/sdk/website/samples.html
OpenCV, "OpenCV computer vision library." [Online]. Available: http://opencv.willowgarage.com/wiki/
C. Augonnet, S. Thibault, R. Namyst, and P.-A. Wacrenier, "StarPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures," In Concurrency and Computation: Practice and Experience, Euro-Par 2009, best papers issue, pp. 863-874, 2009.