Journal of Medical Systems

, Volume 36, Issue 1, pp 145–157 | Cite as

Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review

  • Oliver FaustEmail author
  • Rajendra Acharya U.
  • E. Y. K. Ng
  • Kwan-Hoong Ng
  • Jasjit S. Suri
Original Paper


Diabetes is a chronic end organ disease that occurs when the pancreas does not secrete enough insulin or the body is unable to process it properly. Over time, diabetes affects the circulatory system, including that of the retina. Diabetic retinopathy is a medical condition where the retina is damaged because fluid leaks from blood vessels into the retina. Ophthalmologists recognize diabetic retinopathy based on features, such as blood vessel area, exudes, hemorrhages, microaneurysms and texture. In this paper we review algorithms used for the extraction of these features from digital fundus images. Furthermore, we discuss systems that use these features to classify individual fundus images. The classifications efficiency of different DR systems is discussed. Most of the reported systems are highly optimized with respect to the analyzed fundus images, therefore a generalization of individual results is difficult. However, this review shows that the classification results improved has improved recently, and it is getting closer to the classification capabilities of human ophthalmologists.


Diabetic retinopathy Fundus images Automated detection Blood vessel area Exudes Hemorrhages Microaneurysms Maculopathy 


  1. 1.
    Aboderin, I., Kalache, A., Ben-Shlomo, Y., Lynch, J. W., Yajnik, C. S., Kuh, D., and Yach, D., Life course perspective on coronary heart disease: key issues and implications for policy and research. World Health Organization, Geneva, 2002.Google Scholar
  2. 2.
    Abràmoff, D. M., Niemeijer, M., Suttorp-Schulten, S. A. M., Viergever, A. M., Russell, R. S., and van Ginneken, B., Evaluation of a system for automatic detection of diabetic retinopathy from color fundus photographs in a large population of patients with diabetes. Diabetes Care 31(2):193–198, 2008.CrossRefGoogle Scholar
  3. 3.
    Acharya, U. R., Chua, K. C., Ng, E. Y. K., Wei, W., and Chee, C., Application of higher order spectra for the identification of diabetes retinopathy stages. J. Med. Syst., USA 32(6):431–488, 2008.Google Scholar
  4. 4.
    Acharya, U. R., Lim, C. M., Ng, E. Y. K., Chee, C., and Tamura, T., Computer based detection of diabetes retinopathy stages using digital fundus images. J. Eng. Med. 223(H5):545–553, 2009.CrossRefGoogle Scholar
  5. 5.
    Acharya, U. R., Lim, C. M., Ng, E. Y. K., Chee, C., and Tamura, T., Computer-based detection of diabetes retinopathy stages using digital fundus images. Proc Inst Mech Eng H. 223(5):545–553.Google Scholar
  6. 6.
    Acharya, U. R., Ng, E. Y. K., and Suri, J. S., Image modelling of human eye. Artech House, MA, 2008.Google Scholar
  7. 7.
    Acharya, U. R., Tan, P. H., Subramaniam, T., Tamura, T., Chua, K. C., Goh, S. C., Lim, C. M., Goh, S. Y., Chung, K. R., and Law, C., Automated identification of diabetic type 2 subjects with and without neuropathy using wavelet transform on pedobarograph. J. Med. Syst. 32(1):21–29, 2008.CrossRefGoogle Scholar
  8. 8.
    Alberti, K. G., and Zimmet, P. Z., Definition, diagnosis and classification of diabetes mellitus and its complications, part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet. Med. 15(7):539–553, 1998.CrossRefGoogle Scholar
  9. 9.
    Bernardes, R., Nunes, S., Pereira, I., Torrent, T., Rosa, A., Coelho, D., and Cunha-Vaz, J., Computer-assisted microaneurysm turnover in the early stages of diabetic retinopathy. Ophthalmologica 223(5):284–291, 2009.CrossRefGoogle Scholar
  10. 10.
    Bhuiyan, A., Nath, B., Chua, J., and Kotagiri, R., Blood vessel segmentation from color retinal images using unsupervised texture classification. IEEE Int. Conf. Image Processing, ICIP 5:521–524, 2007.Google Scholar
  11. 11.
    Microaneurysms in diabetic retinopathy. Br. Med. J. 3(5774):548–549, 1971. Scholar
  12. 12.
    Brenner, M. B., Cooper, E. M., de Zeeuw, D., Keane, F. W., Mitch, E. W., Parving, H. H., Remuzzi, G., Snapinn, M. S., Zhang, Z., and Shahinfar, S., Effects of Losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. NEJM 345(12):861–869, 2001.CrossRefGoogle Scholar
  13. 13.
    Chaudhuri, S., Chatterjee, 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(3):263–269, 1989.CrossRefGoogle Scholar
  14. 14.
    Cigna healthcare coverage position- A Report, 2007. Retrieved from: Last accessed on 5th December 2007.
  15. 15.
    Cree, J. M., Leandro, J. J. G., Soares, J. V. B., Cesar, R. M. Jr., Jelinek, H. F., and Cornforth, D., Comparison of various methods to delineate blood vessels in retinal images, Proceedings of the 16th Australian Institute of Physics Congress, Canberra, 2005.Google Scholar
  16. 16.
    Diabetic Retinopathy. Retrieved from: Last accessed on 17th January 2009.
  17. 17.
    Early Treatment Diabetic Retinopathy Study Research Group, Grading diabetic retinopathy from stereoscopic color fundus photographs: an extension of the modified Airlie House classification, ETDRS report number 10. Ophthalmology 98:786–806, 1991.Google Scholar
  18. 18.
    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. 62(3):165–175, 2000.CrossRefGoogle Scholar
  19. 19.
    Englmeier, K. H., Schmid, K., Hildebrand, C., Bichler, S., Porta, M., Maurino, M., and Bek, T., Early detection of diabetes retinopathy by new algorithms for automatic recognition of vascular changes. Eur. J. Med. Res. 9(10):473–488, 2004.Google Scholar
  20. 20.
    Estabridis K, de Figueiredo RJP, Automatic detection and diagnosis of diabetic retinopathy. IEEE Int. Conf. Image Processing, ICIP 2007.Google Scholar
  21. 21.
    Fleming, D. A., Philip, S., Goatman, A. K., Williams, J. G., Olson, A. J., and Sharp, F. P., Automated detection of exudates for diabetic retinopathy screening. Phys. Med. Biol. 52(24):7385–7396, 2007.CrossRefGoogle Scholar
  22. 22.
    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(1):226–229, 2003.CrossRefGoogle Scholar
  23. 23.
    Forracchia, M., Grisan, M. E., and Ruggeri, A., Extraction and quantitative description of vessel features in hypertensive retinopathy fundus images, Presented at CAFIA2001, 2001.Google Scholar
  24. 24.
    Frank, R. N., Diabetic retinopathy. Prog. Retin. Eye Res. 14(2):361–392, 1995.CrossRefGoogle Scholar
  25. 25.
    Fujita, H., Uchiyama, Y., Nakagawa, T., Fukuoka, D., Hatanaka, Y., Hara, T., Lee, G. N., Hayashi, Y., Ikedo, Y., Gao, X., and Zhou, X., Computer-aided diagnosis: the emerging of three CAD systems induced by Japanese health care needs. Comput. Methods Programs Biomed. 92(3):238–248, 2008.CrossRefGoogle Scholar
  26. 26.
    Galloway, M. M., Texture classification using gray level run length. Comput. Graph. Image Process. 4:172–179, 1975.CrossRefGoogle Scholar
  27. 27.
    Gonzalez, R. C., and Woods, R. E., Digital image processing, 2nd edition. Prentice Hall, New Jersey, 2001.Google Scholar
  28. 28.
    Grisan, I. E., Pesce, A., Giani, A., Foracchia, M., and Ruggeri, A., A new tracking system for the robust extraction of retinal vessel structure, 26th Annual International Conference of the IEEE EMBS San Francisco, USA, pp. 1620-1623, 2004.Google Scholar
  29. 29.
    Hayashi, J., Kunieda, T., Cole, J., Soga, R., Hatanaka, Y., Lu, M., Hara, T., and Fujita, F., A development of computer-aided diagnosis system using fundus images, Proceeding of the 7th International Conference on Virtual Systems and MultiMedia (VSMM 2001), pp. 429-438, 2001.Google Scholar
  30. 30.
    Hellstedt, T., and Immonen, I., Disappearance and formation rates of microaneurysms in early diabetic retinopathy. Br. J. Ophthalmol. 80(2):135–139, 1996.CrossRefGoogle Scholar
  31. 31.
    Hoover, A. D., Kouzanetsova, V., and Goldbaum, M., Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imag. 19(3):203–210, 2000.CrossRefGoogle Scholar
  32. 32.
    Hunter, A., Lowell, J., Owens, J., and Kennedy, L, Quantification of diabetic retinopathy using neural networks and sensitivity analysis, In Proceedings of Artificial Neural Networks in Medicine and Biology, pp. 81-86, 2000.Google Scholar
  33. 33.
    International Council of Ophthalmology. International standards: international clinical diabetic retinopathy disease severity scale, detailed table. Retrived from: Last accessed on 17th January 2009.
  34. 34.
    Jalli, P. Y., Hellstedt, T. J., and Immonen, I. J., Early versus late staining of microaneurysms in fluorescein angiography. Retina 17(3):211–215, 1997.CrossRefGoogle Scholar
  35. 35.
    Jelinek, H. J., Cree, M. J., Worsley, D., Luckie, A., and Nixon, P., An automated microaneurysm detector as a tool for identification of diabetic retinopathy in rural optometric practice. Clin. Exp. Optom. 89(5):299–305, 2006.CrossRefGoogle Scholar
  36. 36.
    Kahai, P., Namuduri, K. R., and Thompson, H., A decision support framework for automated screening of diabetic retinopathy. Int. J. Biomed. Imag. 2006:1–8, 2006.Google Scholar
  37. 37.
    Kandiraju, N., Dua, S., and Thompson, H. W., Design and implementation of a unique blood vessel detection algorithm towards early diagnosis of diabetic retinopathy. Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC’05) IEEE Computer Society, pp. 26-31, 2005.Google Scholar
  38. 38.
    Klein, R., Klein, B. E. K., Moss, S. E., Davis, M. D., and DeMets, D. L., The Wisconsin Epidemiologic Study of Diabetic Retinopathy III, prevalence and risk of diabetic retinopathy when age at diagnosis is 30 or more years. Arch. Ophthalmol. 102(4):527–532, 1984.CrossRefGoogle Scholar
  39. 39.
    Kulakarni, D. A., Artificial neural networks for image understanding. Van Nostrand Reinhold, New York, 1993. ISBN:0-442-00921-6.Google Scholar
  40. 40.
    Kumar, A., Diabetic blindness in India: the emerging scenario. Indian J. Ophthalmol. 46(2):65–66, 1998.Google Scholar
  41. 41.
    Larsen, M., Godt, J., Larsen, N., Lund-Andersen, H., Sjolie, A. K., Agardh, E., Kalm, H., Grunkin, M., and Owens, D. R., Automated detection of fundus photographic red lesions in diabetic retinopathy. Invest. Ophthalmol. Vis. Sci. 44(2):761–766, 2003.CrossRefGoogle Scholar
  42. 42.
    Lee, S. C., Lee, E. T., Kingsley, R. M., Wang, Y., Russell, D., Klein, R., and Warn, A., Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts. Arch. Ophthalmol. 119(4):509–515, 2001.Google Scholar
  43. 43.
    Lee, S. C., Lee, E. T., Wang, Y., Klein, R., Kingsley, R. M., and Warn, A., Computer classification of nonproliferative diabetic retinopathy. Arch. Ophthalmol. 123(6):759–764, 2005.CrossRefGoogle Scholar
  44. 44.
    Li, H., and Chutatape, O., Fundus image feature extraction. Proceedings 22nd Annual EMBS International Conference, Chicago, pp. 3071-3073, 2000.Google Scholar
  45. 45.
    Li, H., Hsu, W., Lee, M. L., and Wong, T. Y., Automated grading of retinal vessel caliber. IEEE Trans. Biomed. Eng. 52:1352–1355, 2005.CrossRefGoogle Scholar
  46. 46.
    Li, Q., Jin, X.-M., Gao, Q., You, J., and Bhattacharya, P., Screening diabetic retinopathy through color retinal images. Medical Biometrics 4901:176–183, 2008.CrossRefGoogle Scholar
  47. 47.
    Mirmehdi, M., Xian, X., and Suri, J. S., Hand book of texture analysis. Imperial College Press, UK, 2008.CrossRefGoogle Scholar
  48. 48.
    Nayak, J., Bhat, P. S., Acharya, U. R., Lim, C. M., and Kagathi, M., Automated identification of different stages of diabetic retinopathy using digital fundus images. J. Med. Syst., USA, 32(2):107–115, 2008.Google Scholar
  49. 49.
    Nayak, J., Bhat, P. S., and Acharya, U. R., Automatic identification of diabetic maculopathy stages using fundus images. J. Med. Eng. Technol. 33(2):119–129, 2009.CrossRefGoogle Scholar
  50. 50.
    Neubauer, A. S., Chryssafis, C., Thiel, M., Priglinger, S., Welge-Lussen, U., and Kampik, A., Screening for diabetic retinopathy and optic disc topography with the retinal thickness analyzer. Ophthalmologe 102(3):251–258, 2005.CrossRefGoogle Scholar
  51. 51.
    Nicolai, L., Jannik, G., Michael, G., Henrik, L. A., and Michael, L., Automated detection of diabetic retinopathy in a fundus photographic screening population. Invest. Ophthalmol. Vis. Sci. 44(2):767–771, 2003.CrossRefGoogle Scholar
  52. 52.
    Niemeijer, M., van Ginneken, B., Russell, R. S., Suttorp-Schulten, S. A. M., and Abramoff, D. 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(5):2260–2267, 2007.CrossRefGoogle Scholar
  53. 53.
    Niemeijer, M., van Ginneken, B., Staal, J., Suttorp-Schulten, M., and Abramoff, M., Automatic detection of red lesions in digital color fundus photographs. IEEE Trans. Med. Imag. 24(5):584–592, 2005.CrossRefGoogle Scholar
  54. 54.
    Ong, G. L., Ripley, L. G., Newsom, R. S., Cooper, M., and Casswell, A. G., Screening for sight-threatening diabetic retinopathy: comparison of fundus photography with automated color contrast threshold test. Am. J. Ophthalmol. 137(3):445–452, 2004.CrossRefGoogle Scholar
  55. 55.
    Orbis. Retrieved from: Last accessed December 2009.
  56. 56.
    Osareh, A., Mirmehdi, M., Thomas, B., and Markham, R., Comparative exudate classification using support vector machines and neural networks, The 5th International Conf. on Medical Image Computing and Computer-Assisted Intervention, pp. 413-420, 2002.Google Scholar
  57. 57.
    Philip, S., Fleming, A. D., Goatman, K. A., Fonseca, S., Mcnamee, P., Scotland, G. S., Prescott, G. J., Sharp, P. F., and Olson, J. A., The efficacy of automated “disease/no disease” grading for diabetic retinopathy in a systematic screening programme. Br. J. Ophthalmol. 91(11):1512–1517, 2007.CrossRefGoogle Scholar
  58. 58.
    Phillips, R., Forrester, J., and Sharp, P., Automated detection and quantification of retinal exudates. Graefes Arch. Clin. Exp. Ophthalmol. 231(2):90–94, 1993.CrossRefGoogle Scholar
  59. 59.
    Phillips, R., Spencer, T., Ross, P., Sharp, P., and Forrester, J., Quantification of diabetic maculopathy by digital imaging of the fundus. Eye 5(1):130–137, 1991.CrossRefGoogle Scholar
  60. 60.
    Ramana, K. V., and Ramamoorthy, B., Statistical methods to compare the texture features of machined surfaces. Pattern Recogn. 29:1447–1459, 1996.CrossRefGoogle Scholar
  61. 61.
    Reaven, G. M., Role of insulin resistance in human disease. Diabetes 37:1595–1607, 1988.CrossRefGoogle Scholar
  62. 62.
    Scott, M., Grundy, C., Benjamin, I. J., Burke, G. L., Chait, A., Eckel, R. H., Howard, B. V., Mitch, W., Smith, S. C., and Sowers, J. R., Diabetes and cardiovascular disease. A statement for Healthcare Professionals From the American Heart Association. Circulation 100:1134–1146, 1999.Google Scholar
  63. 63.
    Screening for Diabetic Retinopathy in Europe 15 years after the St. Vincent Declaration. The Liverpool Declaration 2005. Retrieved from: Last accessed on 20th December 2007.
  64. 64.
    Shahidi, M., Ogura, Y., Blair, N. P., and Zeimer, R., Retinal thickness change after focal laser treatment of diabetic macular oedema. Br J Ophthalmol. 78(11):827–830, 1994.CrossRefGoogle Scholar
  65. 65.
    Sinthanayothin, C., Boyce, J. F., Williamson, T. H., and Cook, H. L., Automated detection of diabetic retinopathy on digital fundus image. Diabet. Med. 19(2):105–112, 2002.CrossRefGoogle Scholar
  66. 66.
    Sinthanayothin, C., Kongbunkiat, V., Phoojaruenchanachai, S., and Singalavanija, A., Automated screening system for diabetic retinopathy, 3rd international Symposium on Image and Signal Processing and Analysis 44(2):767-771, 2003.Google Scholar
  67. 67.
    Sopharak, A., and Uyyanonvara, B., Automatic exudates detection from diabetic retinopathy retinal image using fuzzy C-means and morphological methods, Proceedings of the third IASTED international conference Advances in Computer Science and Technology, Thailand, pp. 359-364, 2007.Google Scholar
  68. 68.
    Sopharak, A., Uyyanonvara, B., Barman, S., and Williamson, H. T., Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput. Med. Imaging Graph. 32(8):720–727, 2008.CrossRefGoogle Scholar
  69. 69.
    Tan, J. H., Ng E. Y. K., and Acharya, U. R., Study of normal ocular thermogram using textural parameters. Infrared Phys. Technol. 53(2):120–126, 2009.Google Scholar
  70. 70.
    Vallabha, D., Dorairaj, R., Namuduri, K., and Thompson, H., Automated detection and classification of vascular abnormalities in diabetic retinopathy, Proceedings of 13th IEEE Signals, Systems and Computers 2:1625-1629, 2004.Google Scholar
  71. 71.
    Vujosevic, S., Benetti, E., Massignan, F., Pilotto, E., Varano, M., Cavarzeran, F., Avogaro, A., and Midena, E., Screening for diabetic retinopathy: 1 and 3 nonmydriatic 45-degree digital fundus photographs vs 7 standard early treatment diabetic retinopathy study fields. Am. J. Ophthalmol. 148(1):111–118, 2009.CrossRefGoogle Scholar
  72. 72.
    Walter, T., Massin, P., Erginay, A., Ordonez, R., Jeulin, C., and Klein, J. C., Automatic detection of microaneurysms in color fundus images. Med. Image Anal. 11(6):555–566, 2007.CrossRefGoogle Scholar
  73. 73.
    Wang, H., Hsu, W., Goh, K. G., and Lee, M., An effective approach to detect lesions in colour retinal images, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 181-187, 2000.Google Scholar
  74. 74.
    Watkins, J. P., ABC of diabetes retinopathy. British Medical Journal 326:924–926, 2003.CrossRefGoogle Scholar
  75. 75.
    Wong, L. Y., Acharya, U. R., Venkatesh, Y. V., Chee, C., Lim, C. M., and Ng, E. Y. K., Identification of different stages of diabetic retinopathy using retinal optical images. Information Sciences 178(1):106–121, 2008.CrossRefGoogle Scholar
  76. 76.
    World Diabetes, A newsletter from the World Health Organization, 4, 1998.Google Scholar
  77. 77.
    Zhang, X., and Chutatape, O., Detection and classification of bright lesions in colour fundus images. Int. Conf. on Image Processing 1:139–142, 2004.Google Scholar
  78. 78.
    Zhang, X., and Chutatape, O., Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy. IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2:422–428, 2005.Google Scholar
  79. 79.
    Parving, H. H., Brenner, B. M., Cooper, M. E., de Zeeuw, D., Keane, W. F., Mitch, W. E., Remuzzi, G., Snapinn, S. M., Zhang, Z., and Shahinfar, S., Effect of losartan on renal and cardiovascular complications of patients with type 2 diabetes and nephropathy. Ugeskr. Laeger 163(40):5514–5519, 2001.Google Scholar
  80. 80.
    Samuel, C. L., Elisa, T. L., Yiming, W., Ronald, K., Ronald, M. K., and Ann, W., Computer classification of a nonproliferative diabetic retinopathy. Arch. Ophthalmol. 123:759–764, 2005.CrossRefGoogle Scholar
  81. 81.
    Singalavanija, A., Supokavej, J., Bamroongsuk, P., Sinthanayothin, C., Phoojaruenchanachai, S., and Kongbunkiat, V., Feasibility study on computer-aided screening for diabetic retinopathy. Jpn. J. Ophthalmol. 50(4):361–366, 2006.CrossRefGoogle Scholar
  82. 82.
    Usher, D., Dumskyj, M., Himaga, M., Williamson, T. H., Nussey, S., and Boyce, J., Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet. Med. 21(1):84–90, 2004.CrossRefGoogle Scholar
  83. 83.
    The American Orthopaedic Foot and Ankle Society, 1999 web page: (Last accessed 21.01.2010).
  84. 84.
    Acharya, U. R., Ng, E. Y. K., and Suri, J. S., Image modeling of human eye. Artech House, MA, 2008.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Oliver Faust
    • 1
    Email author
  • Rajendra Acharya U.
    • 1
  • E. Y. K. Ng
    • 2
  • Kwan-Hoong Ng
    • 3
  • Jasjit S. Suri
    • 4
    • 5
  1. 1.Department of Electronics and Computer EngineeringNgee Ann PolytechnicSingaporeSingapore
  2. 2.School of Mechanical and Aerospace Engineering, College of EngineeringNanyang Technological University50 Nanyang AvenueSingapore
  3. 3.Department of Biomedical ImagingUniversity of MalayaKuala LumpurMalaysia
  4. 4.Biomedical TechnologiesDenverUSA
  5. 5.University of IdahoMoscowUSA

Personalised recommendations