Neural network based detection of hard exudates in retinal images

Computer Methods and Programs in Biomedicine

Volumn 93, Issue 1, 2009, Pages 9-19

  García, María ^a   Sánchez, Clara I ^a   López, María I ^b   Abásolo, Daniel ^a   Hornero, Roberto ^a  

^a UNIVERSITY OF VALLADOLID   (Spain)

^b UNIVERSITY OF VALLADOLID   (Spain)

Author keywords

Hard exudate; Multilayer perceptron; Neural network; Radial basis function; Retinal imaging; Support vector machine

Indexed keywords

HARD EXUDATE; MULTILAYER PERCEPTRON; RADIAL BASIS FUNCTION; RETINAL IMAGING; SUPPORT VECTOR MACHINE;

ALDEHYDES; ATTITUDE CONTROL; CLASSIFIERS; ELECTRIC LOADS; EYE PROTECTION; FEEDFORWARD NEURAL NETWORKS; FUNCTIONS; IMAGE ENHANCEMENT; IMAGE RETRIEVAL; IMAGE SEGMENTATION; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; MULTILAYERS; OPHTHALMOLOGY; PROBABILITY DENSITY FUNCTION; RADIAL BASIS FUNCTION NETWORKS; SPONTANEOUS EMISSION; SUGAR (SUCROSE); SUPPORT VECTOR MACHINES; VECTORS;

NEURAL NETWORKS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BIOMETRY; DATABASES, FACTUAL; DIABETIC RETINOPATHY; DIAGNOSIS, COMPUTER-ASSISTED; EXUDATES AND TRANSUDATES; HUMANS; IMAGE ENHANCEMENT; NEURAL NETWORKS (COMPUTER); PREDICTIVE VALUE OF TESTS; RETINA; SENSITIVITY AND SPECIFICITY;

EID: 58149121291     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2008.07.006     Document Type: Article

References (39)

1. Ong G.L., Ripley L.G., Newsom R.S., Cooper M., and Casswel A.G. Screening for sight-threatening diabetic retinopathy: comparison of fundus photography with automated color contrast threshold test. Am. J. Ophthalmol. 137 (2004) 445-452
2. Fong D.S., Aiello L., Gardner T.W., King G.L., Blankenship G., Cavallerano J.D., Ferris F.L., and Klein R. Diabetic retinopathy. Diabetes Care 26 (2003) 226-229
3. Lin D.Y., Blumenkranz M.S., Brothers S.J., and Grosvenor D.M. 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 (2002) 204-213
4. Olson J.A., Strachan F.M., Hipwell J.H., Goatman K.A., McHardy K.C., Forrester J.V., and Sharp P.F. A comparative evaluation of digital imaging, retinal photography and optometrist examination in screening for diabetic retinopathy. Diabet. Med. 20 (2003) 528-534
5. Patton N., Aslam T.M., MacGillivray T., Deary I.J., Dhillon B., Eikelboom R.H., Yogesan K., and Constable I.J. Retinal image analysis: concepts, applications and potential. Prog. Retin. Eye Res. 25 (2006) 99-127
6. Niemeijer M., van Ginneken B., Russell S.R., Suttorp-Schulten M.S.A., and Abràmoff M. 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 (2007) 2260-2267
7. Klein R., Klein B.E., Moss S.E., Davis M.D., and DeMets D.L. The Wisconsin epidemiologic study of diabetic retinopathy VII. Diabetic nonproliferative retinal lesions. Ophthalmology 94 (1987) 1389-1400
8. Ward N.P., Tomlinson S., and Taylor C.J. Image analysis of fundus photographs. The detection and measurement of exudates associated with diabetic retinopathy. Ophthalmology 96 (1989) 80-86
9. Phillips R., Forrester J., and Sharp P. Automated detection and quantification of retinal exudates. Graefe's Arch. Clin. Exp. Ophthalmol. 231 (1993) 90-94
10. Akita K., and Kuga H. A computer method of understanding ocular fundus images. Pattern Recogn. 15 (1982) 431-443
11. Li H., and Chutatape O. Fundus image features extraction. Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago, IL, USA (2000) 3071-3073
12. Li H., and Chutatape O. Automated feature extraction in color retinal images by a model based approach. IEEE Trans. Biomed. Eng. 51 (2004) 246-254
13. Zahlman G., Kochner B., Ugi I., Schuhmann D., Lisenfeld B., Wegner A., Obermaier M., and Mertz M. Hybrid fuzzy image processing for situation assessment: a knowledge-based system for early detection of diabetic retinopathy. IEEE Eng. Med. Biol. Mag. 19 (2000) 76-83
14. Walter T., Klein J.C., Massin P., and Erginay A. 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 (2002) 1236-1243
15. Wang H., Hsu W., Goh K.G., and Lee M.L. An effective approach to detect lesions in color retinal images. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Hilton Head Island, SC, USA (2000) 181-186
16. Ege B.M., Hejlesen O.K., Larsen O.V., Møller K., Jennings B., Kerr D., and Cavan D.A. Screening for diabetic retinopathy using computer based image analysis and statistical classification. Comput. Methods Programs Biomed. 63 (2000) 165-175
17. Sánchez C.I., Hornero R., López M.I., Aboy M., Poza J., and Abásolo D. A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis. Med. Eng. Phys. 30 (2008) 350-357
18. Gardner G.G., Keating D., Williamson T.H., and Elliot A.T. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br. J. Ophthalmol. 80 (1996) 940-944
19. Zhang X., and Chutatape O. Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA (2005) 181-186
20. A. Osareh, Automated identification of diabetic retinal exudates and the optic disc, Ph.D. thesis, Bristol, 2004.
21. Foracchia M., Grisan E., and Ruggeri A. Luminosity and contrast normalization in retinal images. Med. Image Anal. 9 (2005) 179-190
22. Cree M.J., Olson J.A., McHardy K.C., Sharp P.F., and Forrester J.V. The preprocessing of retinal images for the detection of fluorescein leakage. Phys. Med. Biol. 44 (1999) 293-308
23. Grisan E., and Ruggeri A. Segmentation of candidate dark lesions in fundus images based on local thresholding and pixel density. Proceedings of the 29th Annual International Conference of the IEEE EMBS. Lyon, France (2007) 6735-6738
24. Chaudhuri S., Chaterjee S., Katz N., Nelson M., and Goldbaum M. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imag. 8 (1989) 263-269
25. Nadler M., and Smith E. Pattern Recognition Engineering (1993), John Wiley, New York
26. Sonka M., Hlavac V., and Boyle R. Image Processing, Analysis and Machine Vision (1996), International Thomson Computer Press, London
27. Nabney I.T. NETLAB Algorithms for Pattern Recognition (2004), Springer, London
28. Intelligent Data Analysis Group, SVM Toolbox for Matlab, http://www.kernel-machines.org, 2002.
29. Haykin S. Neural Networks: A Comprehensive Foundation (1999), Prentice-Hall International, New Jersey
30. Bishop C.M. Neural Networks for Pattern Recognition (2004), Oxford University Press, New York
31. Hornik K. Approximation capabilities of multilayer feedforward networks. Neural Netw. 4 (1991) 251-257
32. Huang G.B., Chen Y.Q., and Babri H.A. Classification ability of single hidden layer feedforward neural networks. IEEE Trans. Neural Netw. 11 (2000) 799-801
33. Tikhonov A.N. On solving incorrectly posed problems and method of regularization. Doklady Akad. Nauk 151 (1963) 501-504
34. Chen S., Cowan C.F.N., and Grant P.M. Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans. Neural Netw. 2 (1991) 302-309
35. Schölkopf B., Sung K.K., Burges C.J.C., Girosi F., Niyogi P., Poggio T., and Vapnik V. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEE Trans. Signal Process 45 (1997) 2758-2765
36. Moody J. Prediction risk and architecture selection for neural networks. In: Cherkassky V., Friedman J.H., and Wechsler H. (Eds). From Statistics to Neural Networks: Theory and Pattern Recognition Applications (1994), Springer-Verlag, Berlin
37. Moody J.E. The effective number of parameters: an analysis of generalization and regularization in nonlinear learning systems. In: Moody J.E., Hanson S.J., and Lippmann R.P. (Eds). Advances in Neural Information Processing Systems 4 (1992), Morgan Kaufmann, San Mateo 847-854
38. Javitt J.C., Canner J.K., Frank R.G., Steinwachs D.M., and Sommer A. Detecting and treating retinopathy in patients with type I diabetes mellitus. A health policy model. Ophthalmology 97 (1990) 483-494
39. Working Party of the British Diabetic Association, Retinal photography screening for diabetic eye disease, A British Diabetic Association Report, London, 1997.