Detection of Exudates in Retinal Images Based on Computational Intelligence Approach
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R. Klein, B. Klein, S. Moss, M. Davis, and D. Demets, “The Wisconsin epidemiologic study of diabetic retinopathy II. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years,” Arch.Ophthalmol., vol. 102, no. 4, pp. 520–526, 1984.
S. C. Lee, E. T. Lee, Y. Wang, and R. Klein, “Computer classification of nonproliferative diabetic retinopathy,” Arch. Ophthalmol., vol. 123, no. 6, pp. 759–764, 2005.
I. Ghafour, D. Allan, and W. Foulds, “Common causes of pp. 759–764, 2005. blindness and visual handicap in the west of Scotland,” Brit. J. Ophthalmol., vol. 67, no. 4, pp. 209–213, 1983.
R. Phillips, T. Spencer, P. Ross, P. Sharp, and J. Forrester, “Quantification of diabetic maculopathy by digital imaging of the fundus,” Eye, vol. 5, pp. 130–137, 1991.
R. Phillips, J. Forrester, and P. Sharp, “Automated detection and quantification of retinal exudates,” Graefe’s Arch. Clin. Exp. Ophthalmol., vol. 231, pp. 90–94, 1993.
B. Ege, O. Larsen, and O. Hejlesen, “Detection of abnormalities in retinal images using digital image analysis,” in Proc. 11th Scand. Conf. Image Process., 1999, pp. 833–840.
H. Wang, H. Hsu, K. Goh, and M. Lee, “An effective approach to detect lesions in retinal images,” in Proc. IEEE Conf. Comput. Vis. Pattern Recogn., Hilton Head Island, SC, 2000, vol. 2, pp. 181–187.
G. Gardner, D. Keating, T. Williamson, and A. Elliott, “Automatic detection of diabetic retinopathy using an artificial neural network: A screening tool,” Brit. J. Ophthalmol., vol. 80, pp. 940–944, 1996.
A. Hunter, J. Lowell, J. Owens, and L. Kennedy, “Quantification of diabetic retinopathy using neural networks and sensitivity analysis,” in Proc. Artif. Neural Netw. Med. Biol., 2000, pp. 81–86.
C. Sinthanayothin, “Image analysis for automatic diagnosis of diabetic retinopathy,” Ph.D. dissertation, King’s College of London, London, U.K.,1999.
T. Walter, J. Klein, P. Massin, and A. Erginary, “A contribution of image processing to the diagnosis of diabetic retinopathy, detection of exudates in colour fundus images of the human retina,” IEEE Trans. Med. Imag.,vol. 21, no. 10, pp. 1236–1243, Oct. 2002.
M. Niemeijer, B. V. Ginneken, S. R. Russell, M. Suttorp, and M. D. Abramoff, “Automated detection and differentiation of drusen, exudates and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis,” Invest. Ophthalmol. Vis. Sci., vol. 48, pp. 2260–2267, 2007.
M. Goldbaum, S. Moezzi, A. Taylor, and S. Chatterjee, “Automated diagnosis and image understanding with object extraction, object classification and inferencing in retinal images,” in Proc. IEEE Int. Conf. Image Process., Lausanne, Switzerland, Sep. 16–19, 1996, vol. 3, pp. 695–698.
A. Osareh, B. Shadgar, and R. Markham, “Comparative pixel-level exudates recognition in color retinal images,” in International Conference on Image Analysis and Recognition (LNCS, vol. 3656), M. Kamel and A. Campilho, Eds. Toronto, Canada: Springer-Verlag, 2005, pp. 894– 902.
R. Gonzalez and R. Woods, Digital Image Processing. Reading, MA: Addison-Wesley, 1992.
Y. Lim and S. Lee, “On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques,” Pattern Recogn., vol. 23, no. 9, pp. 935–952, 1990.
K. Fukunaga, Statistical Pattern Recognition. New York: Academic, 1990.
S. Sangwine andRHorne, TheColor Image Processing Handbook. London, U.K.: Chapman and Hall, 1998.
A. Witkin, “Scale space filtering,” in Proc. Int. Joint Conf. Artif. Intell.,1983, pp. 1019–1022.
R. Krishnapuram and J. Keller, “A probabilistic approach to clustering,”IEEE Trans. Fuzzy Syst., vol. 1, no. 2, pp. 98–110, May 1993.
J. Dougman, “High confidence visual recognition of persons by a test of statistical independence,” IEEE Trans. Pattern Anal. Mach. Intell.,vol. 15, no. 11, pp. 1148–1161, Nov. 1993.
J. Anil, N. Ratha, and S. Lakshmanan, “Object detection using Gabor filters,” Pattern Recogn., vol. 30, no. 2, pp. 295–309, 1997.
P. Moreno, A. Bernardino, and J. Santos, “Gabor parameter selection for local feature detection,” in Proc. 2nd Iberian Conf. Pattern Recogn. Image Anal.-IBPRIA, 2005, pp. 11–19.
A. Drimbarean and P. F.Whelan, “Experiments in color texture analysis,” Pattern Recogn. Lett., vol. 22, no. 10, pp. 1161–1167, 2001.
O. Nestares, R. Navarro, J. Portilla, and A. Tabernero, “Efficient spatial domain implementation of a multiscale image representation based on Gabor functions,” J. Electron. Imag., vol. 7, no. 1, pp. 166–173, 1998.
C.Bishop, Neural Networks for Pattern Recognition. London, U.K.:Oxford Univ. Press, 1995.
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