A New Parallel Approach for Accelerating the GPU-Based Execution of Edge Detection Algorithms

Zahra Emrani, Soroosh Bateni, Hossein Rabbani

DOI:

Abstract


Real-time image processing is used in a wide variety of applications like those in medical care andindustrial processes. This technique in medical care has the ability to display important patientinformation graphi graphically, which can supplement and help the treatment process. Medical decisionsmade based on real-time images are more accurate and reliable. According to the recent researches,graphic processing unit (GPU) programming is a useful method for improving the speed and quality ofmedical image processing and is one of the ways of real-time image processing. Edge detection is anearly stage in most of the image processing methods for the extraction of features and object segmentsfrom a raw image. The Canny method, Sobel and Prewitt filters, and the Roberts’ Cross technique aresome examples of edge detection algorithms that are widely used in image processing and machinevision. In this work, these algorithms are implemented using the Compute Unified Device Architecture(CUDA), Open Source Computer Vision (OpenCV), and Matrix Laboratory (MATLAB) platforms. Anexisting parallel method for Canny approach has been modified further to run in a fully parallel manner.This has been achieved by replacing the breadth-first search procedure with a parallel method. Thesealgorithms have been compared by testing them on a database of optical coherence tomography images.The comparison of results shows that the proposed implementation of the Canny method on GPU usingthe CUDA platform improves the speed of execution by 2–100× compared to the central processing unitbasedimplementation using the OpenCV and MATLAB platforms.

Keywords


Algorithms; computer systems; computers; humans computer-assisted; image processing; optical coherence tomography

Full Text:

PDF

References


Che S, Boyer M, Meng J, Tarjan D, Sheaffer JW, Skadron K.A performance study of general purpose applications on graphics processors using CUDA. J Parallel Distrib Comput 2008;68:1370-80.

Cope B, Cheung PY, Luk W, Howes L. Performance comparison of

graphics processors to reconfigurable logic: A case study. IEEE Trans Comput 2010;59:433-48.

Moreland K, Angel E. The FFT on a GPU. Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Conference on Graphics Hardware, 2003. p. 112-9.

Strzodka R, Garbe C. Real-time motion estimation and visualization on graphics cards. Proceedings of the Conference on Visualization’04, 2004. p. 545-52.

Shen G, Gao G-P, Li S, Shum H-Y, Zhang Y-Q. Accelerate video decoding with generic GPU. IEEE Trans Circuits Syst Video Technol 2005;15:685-93.

http://www.gpu4vision.org.

Fung J, Mann S. OpenVIDIA: Parallel GPU computer vision. Proceedings of the 13th Annual ACM International Conference on Multimedia, 2005. p. 849-52.

Allusse Y, Horain P, Agarwal A, Saipriyadarshan C. GpuCV: An opensource GPU-accelerated framework for image processing and computer vision. Proceedings of the 16th ACM International Conference on Multimedia, 2008. p. 1089-92.

Babenko P, Shah M. MinGPU: A minimum GPU library for computer vision. J Real-Time Image Process 2008;3:255-68.

Zhuo Y, Wu X-L, Haldar JP, Hwu W-M, Liang Z-P, Sutton BP. Accelerating iterative field-compensated MR image reconstruction on GPUs. 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE, 2010. p. 820-3.

Yang J, Feng C, Zhao D. A CUDA-based reverse gridding algorithm for MR reconstruction. Magn Reson Imaging 2013;31:313-23.

Stone SS, Haldar JP, Tsao SC, Hwu W-M, Sutton BP, Liang Z-P. Accelerating advanced MRI reconstructions on GPUs. J Parallel Distrib Comput 2008;68:1307-18.

Kim K, Park S, Hong H, Shin YG. Fast 2D-3D registration using GPU-based preprocessing. Proceedings of 7th International Workshop on Enterprise Networking and Computing in Healthcare Industry, HEALTHCOM’05, 2005. p. 139-43.

Hwu W, Nandakumar D, Haldar J, Atkinson IC, Sutton B, Liang Z P, et al. Accelerating MR image reconstruction on GPUs. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI09, 2009. p. 1283-86.

Huang T-Y, Tang Y-W, Ju S-Y. Accelerating image registration of MRI by GPU-based parallel computation. Magn Reson Imaging 2011;29:712-6.

Han X, Hibbard LS, Willcut V. GPU-accelerated, gradient-free MI deformable registration for atlas-based MR brain imag segmentation. IEEE Computer Society Conference on ComputerVision and Pattern Recognition Workshops, CVPR Workshops09, 2009. p. 141-8.

Gong HX, Hao L. Roberts edge detection algorithm based on GPU. J Chem Pharm Res 2014;6:1308-14.

Sarkar S, Venugopalan V, Reddy K, Giering M, Ryde J, Jaitly N. Occlusion edge detection in RGB-D frames using deep convolutional networks. CoRRabs/1412.7007, 2014.

Chouchene M, Sayadi FE, Said Y, Atri M, Tourki R. Efficient implementation of Sobel edge detection algorithm on CPU, GPU and FPGA. Int J Adv Media Commun 2014;5:105-17.

Ogawa K, Ito Y, Nakano K. Efficient Canny edge detection using a GPU. IEEE First International Conference on Networking and Computing (ICNC), Higashi-Hiroshima, 2010. p. 279-80.

Niu S, Yang J, Wang S, Chen G. Improvement and parallel implementation of canny edge detection algorithm based on GPU. IEEE 9th International Conference on ASIC (ASICON), October 25-28, 2011. p. 641-4.

Roodt Y, Visser W, Clarke W. Image processing on the GPU: Implementing the Canny edge detection algorithm. International Symposium of the Pattern Recognition Association of South Africa, 2007. p. 1-6.

Luo Y, Duraiswami R. Canny edge detection on NVIDIA CUDA. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW08, 2008. p. 1-8.

CannyJ.Acomputational approach to edge detection. IEEE Transactios on Pattern Analysis and Machine Intelligence, 1986. p. 679-98.

Sobel I, Feldman G. A 3×3 isotropic gradient operator for image processing. A Talk at the Stanford Artificial Project, 1968. p. 271-2.

Sobel I. Camera models and machine perception. DTI Document, 1970.

Prewitt JM. Object Enhancement and Extraction, vol 75 NewYork: Academic Press 1970.

Roberts L. In: Tippet J, editor. Machine Perception of Three Dimensional Solids, Optical and Electro-Optical Information Processing. Cambridge, MA: IT Press; 1965.

Trucco E, Verri A. Introductory Techniques for 3-D Computer Vision, vol 93. Englewood Cliffs: Prentice Hall; 1998.

Jain R, Kasturi R, Schunck BG. Machine Vision, vol 5 New York: McGraw-Hill 1995.

Rabbani H, Gazor S. Image denoising employing local mixture models in sparse domains. IET Image Processing 2010;4:413-28.

Skiena S. The Algorithm Design Manual. Springer; 2008. p. 480 doi: 10.1007/978-1-84800-070-4-4

Leiserson CE, Schardl TB. A work-efficient parallel breadth-first search algorithm (or how to cope with the nondeterminism of reducers). ACM Symposium on Parallelism in Algorithms and Architectures, 2010.

Lee CY. An algorithm for path connections and its applications. IRE Transactions on Electronic Computers; 1961. http://www. heidelbergengineering.com/us/wp-content/uploads/hra2-aquirethe perfect-image.pdf.

Danesh H, Kafieh R, Rabbani H, Hajizadeh F. Segmentation of choroidal boundary in enhanced depth imaging OCTs using a multiresolution texture based modeling in graph cuts. Computational and Mathematical Methods in Medicine, vol 2014.


Refbacks

  • There are currently no refbacks.


 

  https://e-rasaneh.ir/Certificate/22728

https://e-rasaneh.ir/

ISSN : 2228-7477