Neural network based detection of hard exudates in retinal images
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Ong, 2004, Screening for sight-threatening diabetic retinopathy: comparison of fundus photography with automated color contrast threshold test, Am. J. Ophthalmol., 137, 445, 10.1016/j.ajo.2003.10.021
Lin, 2002, The sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: a comparison with ophthalmoscopy and standardized mydriatic color photography, Am. J. Ophthalmol., 134, 204, 10.1016/S0002-9394(02)01522-2
Olson, 2003, A comparative evaluation of digital imaging, retinal photography and optometrist examination in screening for diabetic retinopathy, Diabet. Med., 20, 528, 10.1046/j.1464-5491.2003.00969.x
Patton, 2006, Retinal image analysis: concepts, applications and potential, Prog. Retin. Eye Res., 25, 99, 10.1016/j.preteyeres.2005.07.001
Niemeijer, 2007, Automated detection and differentiation of drusen, exudates, and cotton–wool spots in digital color fundus photographs for diabetic retinopathy diagnosis, Invest. Ophthalmol. Vis. Sci., 48, 2260, 10.1167/iovs.06-0996
Klein, 1987, The Wisconsin epidemiologic study of diabetic retinopathy VII. Diabetic nonproliferative retinal lesions, Ophthalmology, 94, 1389, 10.1016/S0161-6420(87)33275-0
Ward, 1989, Image analysis of fundus photographs. The detection and measurement of exudates associated with diabetic retinopathy, Ophthalmology, 96, 80, 10.1016/S0161-6420(89)32925-3
Phillips, 1993, Automated detection and quantification of retinal exudates, Graefe's Arch. Clin. Exp. Ophthalmol., 231, 90, 10.1007/BF00920219
Akita, 1982, A computer method of understanding ocular fundus images, Pattern Recogn., 15, 431, 10.1016/0031-3203(82)90022-X
Li, 2000, Fundus image features extraction, 3071
Li, 2004, Automated feature extraction in color retinal images by a model based approach, IEEE Trans. Biomed. Eng., 51, 246, 10.1109/TBME.2003.820400
Zahlman, 2000, Hybrid fuzzy image processing for situation assessment: a knowledge-based system for early detection of diabetic retinopathy, IEEE Eng. Med. Biol. Mag., 19, 76, 10.1109/51.816246
Walter, 2002, A contribution of image processing to the diagnosis of diabetic retinopathy—detection of exudates in color fundus images of the human retina, IEEE. Trans. Med. Imag., 21, 1236, 10.1109/TMI.2002.806290
Wang, 2000, An effective approach to detect lesions in color retinal images, 181
Ege, 2000, Screening for diabetic retinopathy using computer based image analysis and statistical classification, Comput. Methods Programs Biomed., 63, 165, 10.1016/S0169-2607(00)00065-1
Sánchez, 2008, A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis, Med. Eng. Phys., 30, 350, 10.1016/j.medengphy.2007.04.010
Gardner, 1996, Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool, Br. J. Ophthalmol., 80, 940, 10.1136/bjo.80.11.940
Zhang, 2005, Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy, 181
A. Osareh, Automated identification of diabetic retinal exudates and the optic disc, Ph.D. thesis, Bristol, 2004.
Foracchia, 2005, Luminosity and contrast normalization in retinal images, Med. Image Anal., 9, 179, 10.1016/j.media.2004.07.001
Cree, 1999, The preprocessing of retinal images for the detection of fluorescein leakage, Phys. Med. Biol., 44, 293, 10.1088/0031-9155/44/1/021
Grisan, 2007, Segmentation of candidate dark lesions in fundus images based on local thresholding and pixel density, 6735
Chaudhuri, 1989, Detection of blood vessels in retinal images using two-dimensional matched filters, IEEE Trans. Med. Imag., 8, 263, 10.1109/42.34715
Nadler, 1993
Sonka, 1996
Nabney, 2004
Intelligent Data Analysis Group, SVM Toolbox for Matlab, http://www.kernel-machines.org, 2002.
Haykin, 1999
Bishop, 2004
Hornik, 1991, Approximation capabilities of multilayer feedforward networks, Neural Netw., 4, 251, 10.1016/0893-6080(91)90009-T
Huang, 2000, Classification ability of single hidden layer feedforward neural networks, IEEE Trans. Neural Netw., 11, 799, 10.1109/72.846750
Tikhonov, 1963, On solving incorrectly posed problems and method of regularization, Doklady Akad. Nauk, 151, 501
Chen, 1991, Orthogonal least squares learning algorithm for radial basis function networks, IEEE Trans. Neural Netw., 2, 302, 10.1109/72.80341
Schölkopf, 1997, Comparing support vector machines with Gaussian kernels to radial basis function classifiers, IEE Trans. Signal Process, 45, 2758, 10.1109/78.650102
Moody, 1994, Prediction risk and architecture selection for neural networks
Moody, 1992, The effective number of parameters: an analysis of generalization and regularization in nonlinear learning systems, 847
Javitt, 1990, Detecting and treating retinopathy in patients with type I diabetes mellitus. A health policy model, Ophthalmology, 97, 483, 10.1016/S0161-6420(90)32573-3
Working Party of the British Diabetic Association, Retinal photography screening for diabetic eye disease, A British Diabetic Association Report, London, 1997.