A GPU-based Evolution Strategy for Optic Disk Detection in Retinal Images
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- Articles
- Submitted: June 20, 2016
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Published: July 14, 2016
Abstract
Parallel processing using graphic processing units (GPUs) has attracted much research interest in recent years. Parallel computation can be applied to evolution strategy (ES) for processing individuals in a population, but evolutionary strategies are time consuming to solve large computational problems or complex fitness functions. In this paper we describe the implementation of an improved ES for optic disk detection in retinal images using the Compute Unified Device Architecture (CUDA) environment. In the experimental results we show that the computational time for optic disk detection task has a speedup factor of 5x and 7x compared to an implementation on a mainstream CPU.References
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[2] N. Hansen, D. V. Arnold, and A. Auger, “Evolution Strategies,” in Springer Handbook of Computational Intelligence, J. Kacprzyk and W. Pedrycz, Eds. Springer Berlin Heidelberg, 2015, pp. 871–898.
[3] G. Zeng and C. Ding, “An Analysis on Parallel Genetic Algorithm,” Computer Engineering, vol. 27, no. 9, pp. 53–55, 2001.
[4] Y. Ke, Y. Li, and D. Li, “Image Matching Using Genetic Algorithm on GPU,” in 2011 International Conference on Control, Automation and Systems Engineering (CASE), 2011, pp. 1–4.
[5] NVIDIA CUDA Compute Unified Device Architecture - Programming Guide. 2007.
[6] J. Sanders and E. Kandrot, CUDA by Example: An Introduction to General-Purpose GPU Programming, 1st ed. Addison-Wesley Professional, 2010.
[7] David Kirk and Wen-mei Hwu, Programming Massively Parallel Processors, 2nd ed. 2012.
[8] D. Robilliard, V. Marion-Poty, and C. Fonlupt, “Population Parallel GP on the G80 GPU,” in Genetic Programming, M. O’Neill, L. Vanneschi, S. Gustafson, A. I. E. Alcázar, I. D. Falco, A. D. Cioppa, and E. Tarantino, Eds. Springer Berlin Heidelberg, 2008, pp. 98–109.
[9] G. Chen, D. Xu, H. Hu, Y. Liu, and R. Chen, “The Application of CUDA Technology in Biomedical Image Processing,” in Emerging Research in Artificial Intelligence and Computational Intelligence, J. Lei, F. L. Wang, H. Deng, and D. Miao, Eds. Springer Berlin Heidelberg, 2012, pp. 378–385.
[10] M. A. Khan and A. Juhn, “Diabetic Retinopathy,” in Optical Coherence Tomography, A. Girach and R. C. Sergott, Eds. Springer International Publishing, 2016, pp. 29–42.
[11] L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y. Li, S. Garg, K. W. Tobin Jr., and E. Chaum, “Exudate-based diabetic macular edema detection in fundus images using publicly available datasets,” Medical Image Analysis, vol. 16, no. 1, pp. 216–226, Jan. 2012.
[12] M. Krause, R. M. Alles, B. Burgeth, and J. Weickert, “Fast retinal vessel analysis,” J Real-Time Image Proc, vol. 11, no. 2, pp. 413–422, Apr. 2016.
[13] O. S. Soliman, J. Platoš, A. E. Hassanien, and V. Snášel, “Automatic Localization and Boundary Detection of Retina in Images Using Basic Image Processing Filters,” in Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011, M. Kudělka, J. Pokorný, V. Snášel, and A. Abraham, Eds. Springer Berlin Heidelberg, 2013, pp. 169–182.
[14] G. Sánchez Torres, A. Espinosa Bedoya, and Y. Fernando Ceballos, “DETECCIÓN DEL DISCO ÓPTICO EN RETINOGRAFÍAS MEDIANTE UNA ESTRATEGIA EVOLUTIVA (µ+λ),” Revista EIA, no. 21, pp. 55–66, Jun. 2014.
[15] M. D. Abramoff and M. Niemeijer, “The automatic detection of the optic disc location in retinal images using optic disc location regression,” in 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006. EMBS ’06, 2006, pp. 4432–4435.
[16] S. Sb and V. Singh, “Automatic Detection of Diabetic Retinopathy in Non-dilated RGB Retinal Fundus Images,” International Journal of Computer Applications, vol. 47, no. 19, pp. 26–32, Jun. 2012.
[17] C. Sinthanayothin, J. Boyce, H. Cook, and T. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” Br J Ophthalmol, vol. 83, no. 8, pp. 902–910, Aug. 1999.
[18] A. Hoover and M. Goldbaum, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” IEEE Transactions on Medical Imaging, vol. 22, no. 8, pp. 951–958, Aug. 2003.
[19] C. Trujillo and J. Garcia-Sucerquia, “Graphics Processing Units: More Than the Pathway to Realistic Video-Games,” Dyna, vol. 78, no. 168, pp. 164–172, 2011.
[20] L. Zheng, Y. Lu, M. Ding, Y. Shen, M. Guoz, and S. Guo, “Architecture-based Performance Evaluation of Genetic Algorithms on Multi/Many-core Systems,” in 2011 IEEE 14th International Conference on Computational Science and Engineering (CSE), 2011, pp. 321–334.
[21] V. Kalesnykiene, J. –. Kamarainen, R. Voutilainen, J. Pietilä, H. Kälviäinen, and H. Uusitalo, DIARETDB1 diabetic retinopathy database and evaluation protocol. 2012.
[22] Narendra V G and Hareesh K S, “Study and comparison of various image edge detection techniques used in quality inspection and evaluation of agricultural and food products by computer vision,” Int J Agric & Biol Eng, vol. 4, pp. 83–90.
[23] W. B. Langdon, “A Fast High Quality Pseudo Random Number Generator for nVidia CUDA,” in Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, New York, NY, USA, 2009, pp. 2511–2514.
