Advertisement

Major automatic diabetic retinopathy screening systems and related core algorithms: a review

  • Di XiaoEmail author
  • Alauddin Bhuiyan
  • Shaun Frost
  • Janardhan Vignarajan
  • Mei-Ling Tay-Kearney
  • Yogesan Kanagasingam
Original Paper
  • 38 Downloads

Abstract

Diabetic retinopathy (DR), one of the major and long-term microvascular complications of diabetes, is the most common cause of vision loss and blindness in the working population of the world. Even with the management of diabetes, most patients will develop some forms of DR after approximately 20 years. However, DR is a treatable disease throughout the disease progression. To provide appropriate DR management, the USA and European countries have successfully implemented systematic early DR screening programs. At the same time, some computer-aided DR screening systems, which combine advanced DR detection algorithms and telemedicine technology, have also been developed for early-stage DR detection. Some of them have been tested in the DR screening programs. In this paper, we focus on a review of the major automatic DR screening systems which have performed large-scale evaluation rather than give an extensive review of all published DR grading algorithms. We first present the structures of the automatic systems and their supporting algorithms developed by the research groups, as well as the practices of the systems in their screening programs. We further present a more detailed review of the DR lesion detection algorithms in each system and reveal how the DR screening systems successfully practiced in clinical trials or large-scale screening programs. We also review recently new research areas as well as deep learning-based DR screening systems and compare them with the traditional lesion detection-based DR screening systems. The performances of the systems in the trials are summarized by considering the specificity and sensitivity with respect to the scale of testing datasets. At last, we will discuss future challenges.

Keywords

Diabetic retinopathy Automatic DR screening system DR grading algorithm DR screening program 

Notes

Acknowledgements

We would like to thank NHMRC Australia and Diabetes Research Western Australia for funding the project (NHMRC Development Grant APP1093682, Diabetes Research Western Australia Grant 2018).

References

  1. 1.
    Juutilainen, A., Lehto, S., Rönnemaa, T., Pyörälä, K., Laakso, M.: Retinopathy predicts cardiovascular mortality in type 2 diabetic men and women. Diabetes Care 30(2), 292–299 (2007)Google Scholar
  2. 2.
    Wong, T.Y., et al.: Retinopathy and risk of congestive heart failure. JAMA 293, 63–69 (2005)Google Scholar
  3. 3.
    Edwards, M.S., et al.: Associations between retinal microvascular abnormalities and declining renal function in the elderly population: the Cardiovascular Health Study. Am. J. Kidney Dis. 46(2), 214–224 (2005)MathSciNetGoogle Scholar
  4. 4.
    Ramanathan, R.S.: Correlation of duration, hypertension and glycemic control with microvascular complications of diabetes mellitus at a tertiary care hospital. Integr. Mol. Med. 4 (2017).  https://doi.org/10.15761/IMM.1000272
  5. 5.
    WHO Media Centre, “Diabetes” http://www.who.int/diabetes/en/ (2018). Accessed Mar 2018
  6. 6.
    Baker IDI Heart & Diabetes Institute, “Diabetes: the silent pandemic and its impact on Australia,” http://www.diabetesaustralia.com.au/Documents/DA/What’s%20New/12.03.14%20Diabetes%20management%20booklet%20FINAL.pdf (2018). Accessed Mar 2018
  7. 7.
    Pan American Health Organization, “Prevention of Blindness and Eye Care - Blindness”, http://new.paho.org/hq/index.php?option=com_content&view=article&id=244&Itemid=1&lang=en&limitstart=1 (2018). Accessed Mar 2018
  8. 8.
    Xiong, Y., Liu, L., Chen, Y., Zhao, J.: Survey on the awareness of diabetic retinopathy among people with diabetes in the Songnan community of Shanghai. Int. Eye Sci. 15(7), 1117–1122 (2015)Google Scholar
  9. 9.
    Rubina, H., et al.: Diabetic retinopathy awareness and practices in a low-income suburban population in Karachi, Pakistan. J. Diabetol. 8(2), 49–55 (2017)Google Scholar
  10. 10.
    Happich, M., et al.: The economic burden of diabetic retinopathy in Germany in 2002. Graefe’s Arch. Clin. Exp. Ophthalmol. 246(1), 151–159 (2008)Google Scholar
  11. 11.
    Hazin, R., et al.: Revisiting diabetes 2000: challenges in establishing nationwide diabetic retinopathy prevention programs. Am. J. Ophthalmol. 152, 723–729 (2011)Google Scholar
  12. 12.
    Deb, N., et al.: Screening for diabetic retinopathy in France. Diabetes Metab. 30(2), 140–5 (2004)Google Scholar
  13. 13.
    Heaven, C.J., Cansfield, J., Shaw, K.M.: A screening programme for diabetic retinopathy. Pract. Diabetes Int. 9(2), 43–45 (1992)Google Scholar
  14. 14.
    Jones, S., Edwards, R.T.: Diabetic retinopathy screening: a systematic review of the economic evidence. Diabet. Med. 27(3), 249–256 (2010)Google Scholar
  15. 15.
    Teng, T., Lefley, M., Claremont, D.: Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy. Med. Biol. Eng. Comput. 40(1), 2–13 (2002)Google Scholar
  16. 16.
    Narasimhan, K., Neha, V.C., Vjayarekha, K.: A review of automated diabetic retinopathy diagnosis from fundus image. J. Theor. Appl. Inf. Technol. 39(2), 225–238 (2012)Google Scholar
  17. 17.
    Faust, O., et al.: Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. J. Med. Syst. 36(1), 145–157 (2012)MathSciNetGoogle Scholar
  18. 18.
    Mookiah, M.R.K., et al.: Computer-aided diagnosis of diabetic retinopathy: a review. Comput. Biol. Med. 43(12), 2136–2155 (2013)Google Scholar
  19. 19.
    Shingade, A.P., Kasetwar, A.R.: A review on implementation of algorithms for detection of diabetic retinopathy. Int. J. Res. Eng. Technol. 3(3), 87–94 (2014)Google Scholar
  20. 20.
    Kauppi, T.: The DIARETDB1 diabetic retinopathy database and evaluation protocol, In: Proceedings of the 11th Conference on Medical Image Understanding and Analysis, Aberystwyth, Wales (2007)Google Scholar
  21. 21.
    Early Treatment Diabetic Retinopathy Study Research Group (ETDRS), Early photocoagulation for diabetic retinopathy. ETDRS report number 9, Ophthalmology 98, pp. 766–785 (1991)Google Scholar
  22. 22.
    Early Treatment Diabetic Retinopathy Study Research Grou (ETDRS), Grading diabetic retinopathy from stereoscopic color fundus photographs–an extension of the modified Airlie House classification. ETDRS report number 10. Early Treatment Diabetic Retinopathy Study Research Group, Ophthalmology 98(5 Suppl), pp. 786–806 (1991)Google Scholar
  23. 23.
    American Academy of Ophthalmology, Diabetic Retinopathy PPP-Updated 2017, http://www.aao.org/preferred-practice-pattHrBern/diabetic-retinopathy-ppp-updated-2017HrB (2018). Accessed Mar 2018
  24. 24.
    Al-Diri, B. et al.: REVIEW—a reference data set for retinal vessel profiles. In: Conference Proceedings on IEEE Engineering in Medicine and Biology Society 2262–5 (2008)Google Scholar
  25. 25.
    Usher, D., et al.: Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet. Med. 21(1), 84–90 (2004)Google Scholar
  26. 26.
    Gardner, G.G., et al.: Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Br. J. Ophthalmol. 80(11), 940–944 (1996)Google Scholar
  27. 27.
    Sinthanayothin, C. et al.: Automated screening system for diabetic retinopathy. In: Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis, pp. 915–920 (2003)Google Scholar
  28. 28.
    Singalavanija, A., et al.: Feasibility study on computer-aided screening for diabetic retinopathy. Jpn. J. Ophthalmol. 50, 361–366 (2006)Google Scholar
  29. 29.
    Newsom, R.S., et al.: Clinical evaluation of ’local contrast enhancement’ for oral fluorescein angiograms. Eye 14, 318–323 (2000)Google Scholar
  30. 30.
    Sinthanayothin, C., et al.: Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br. J. Ophthalmol. 83, 902–910 (1999)Google Scholar
  31. 31.
    Toulson, D.L., Boyce, J.F.: Segmentation of MR images using neural nets. Image Vis. Comput. 10, 324–328 (1992)Google Scholar
  32. 32.
    Sinthanayothin, C., et al.: Automated detection of diabetic retinopathy on digital fundus images. Diabet. Med. 19, 105–112 (2002)Google Scholar
  33. 33.
    Torrent-Solans, T., et al.: Red-dots counting on digitalized fundus images of mild nonproliferative retinopathy in Diabetes type 2. Invest. Ophthalmol. Vis. Sci. 45(13), 2985 (2004)Google Scholar
  34. 34.
    Sjolie, A.K., et al.: Retinal microaneurysm count predicts progression and regression of diabetic retinopathy. Post-hoc results from the DIRECT programme. Diabet. Med. 28(3), 345–351 (2011)Google Scholar
  35. 35.
    Bernardes, R., et al.: Computer-assisted microaneurysm turnover in the early stages of diabetic retinopathy. Ophthalmologica 223(5), 284–291 (2009)Google Scholar
  36. 36.
    Sharp, P.F., et al.: The value of digital imaging in diabetic retinopathy. Health Tehnol. Assess. 7(30), 1–119 (2003)Google Scholar
  37. 37.
    Cunha-Vaz, J., et al.: Computer aided detection of diabetic retinopathy progression. In: Yogesan, K., Goldschmidt, L., Cuadros, J. (eds.) Digital Teleretinal screening, vol. 6, pp. 59–66. Springer, Berlin (2012)Google Scholar
  38. 38.
    Dias, J.M.P., Oliveira, C.M., Cruz, L.: Retinal image quality assessment using generic image quality indicators. Inf. Fusion 19, 73–90 (2014)Google Scholar
  39. 39.
    Ribeiro, L., Oliveira, C.M., Neves, C., Ramos, J.D., Ferreira, H., Cunha-Vaz, J.: Screening for diabetic retinopathy in the central region of Portugal. Added value of automated disease/no disease grading. Ophthalmologica 233, 96–103 (2015)Google Scholar
  40. 40.
    Ferreira, J. F. et al.: Earmarking retinal changes in a sequence of digital color fundus photographs. In: Proceedings of the 3rd European medical and biological engineering conference vol. 11, pp. 924-1 to 1924-6, Prague: IFMBE (2005)Google Scholar
  41. 41.
    Petrou, M., Bosdogianni, P.: Image Processing—The Fundamentals. Wiley, Chichester (1999)Google Scholar
  42. 42.
    Bernardes, R., et al.: Multimodal functional and morphological nonrigid image registration. IEEE Int. Conf. Image Process. 1, 1133–1136 (2005)Google Scholar
  43. 43.
    Pearson, J. et al.: Video-rate image correlation processor. In: The Proceedings of SPIE 0119: Applications of Digital Image Processing, A.G. Tescher (ed) 119, pp. 197–205 (1977)Google Scholar
  44. 44.
    Rangarajan, L., Chui, H., Bookstein, F.: The soft assign procrustes matching algorithm. In: Duncan, J., Gindi G. (eds.) Proceedings of Information Processing in Medical Imaging, Springer-Verlag Berlin Heidelberg, 1230, pp. 29–42 (1997)Google Scholar
  45. 45.
    Oliveira, C.M., et al.: Improved automated screening of diabetic retinopathy. Ophthalmologica 226(4), 191–197 (2011)Google Scholar
  46. 46.
    Fleming A.D., et al.: Response to ‘Improved automated screening of diabetic retinopathy’ by Carlos M. Oliveira et al. Ophthalmologica 227(3), 173 (2012)Google Scholar
  47. 47.
    Karnowski, T.P., et al.: Automated image analysis and the application of diagnostic algorithms in an ocular telehealth network. In: Yogesan, K., Goldschmidt, L., Cuadros, J. (eds.) Digit. Teleretinal Screen., pp. 43–57. Springer, Berlin Heidelberg (2012)Google Scholar
  48. 48.
    Usher, D.B., Himaga, M., Dumskyj, M.J.: Automated assessment of digital fundus image quality using detected vessel area. In: Proceeding of Medical Image Understanding and Analysis, Bristish Machine Vision Association 81–84. BMVA) Sheffield, UK (2003)Google Scholar
  49. 49.
    Fleming, A.D., et al.: Automated assessment of diabetic retinal image quality based on clarity and field definition. Invest. Ophthalmol. Vis. Sci. 47(3), 1120–1125 (2006)MathSciNetGoogle Scholar
  50. 50.
    Niemeijer, M., Abràmoff, M.D., van Ginneken, B.: Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med. Image Anal. 10(6), 888–898 (2006)Google Scholar
  51. 51.
    Giancardo, L., et al.: Elliptical local vessel density: a fast and robust quality metric for fundus images. Proc. IEEE Eng. Med. Biol. Soc. 35, 34–37 (2008)Google Scholar
  52. 52.
    Giancardo, L. et al.: Quality assessment of retinal fundus images using elliptical local vessel density. In: Campolo, D. (eds.) New Developments in Biomedical Engineering, chapter 11, pp. 201–223, INTECH (2010)Google Scholar
  53. 53.
    Zana, F., Klein, J.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Image Process. 10(7), 1010–1019 (2001)zbMATHGoogle Scholar
  54. 54.
    Karnowski, T.P., et al.: Locating the optic nerve in retinal images: comparing model-based and Bayesian decision methods. Proc. IEEE Eng. Med. Biol. Soc. 1, 4436–4439 (2006)Google Scholar
  55. 55.
    Tobin, K.W., et al.: Detection of anatomic structures in human retinal imagery. IEEE Trans. Med. Imaging 26, 1729–1739 (2007)Google Scholar
  56. 56.
    Giancardo, L., et al.: Microaneurysms detection with the radon cliff operator in retinal fundus images. In: Medical Imaging 2010: Image Processing, Proceedings of SPIE, vol. 7623 (2010).  https://doi.org/10.1117/12.844442
  57. 57.
    Quellec, G.: Optimal wavelet transform for the detection of microaneurysms in retina photographs. IEEE Trans. Med. Imaging 27(9), 1230–1241 (2008)Google Scholar
  58. 58.
    Giancardo, L.: Bright Retinal Lesions Detection Using Color Fundus Images Containing Reflective Features. In: Dössel, O., Schlegel, C.W. (eds.) Proceedings of World Congress of Medical Physics And Biomedical Engineering, vol. 25, pp. 292–294. Springer, Berlin Heidelberg (2009)Google Scholar
  59. 59.
    Tobin, K. W.: Using a patient image archive to diagnose retinopathy. In: Proceedings of 30th Annual International IEEE EMBS Conference, pp. 5441–5444 (2008)Google Scholar
  60. 60.
    Chaum, E.: Automated diagnosis of retinopathy by content-based image retrieval. Retina 28, 1463–1477 (2008)Google Scholar
  61. 61.
    Abràmoff, M.D., Niemeijer, M., Russell, S.R.: Automated detection of diabetic retinopathy: barriers to translation into clinical practice. Expert Rev. Med. Devices 7(2), 287–296 (2010)Google Scholar
  62. 62.
    Abràmoff, M.D.: Automated early detection of diabetic retinopathy. Ophthalmology 117(6), 1147–1154 (2010)Google Scholar
  63. 63.
    Niemeijer, M., Abràmoff, M.D., van Ginneken, B.: Information fusion for diabetic retinopathy cad in digital color fundus photographs. IEEE Trans. Med. Imaging 28(5), 775–785 (2009)Google Scholar
  64. 64.
    Niemeijer, M., Abràmoff, M.D., van Ginneken, B.: Fast Detection of the Optic Disc and Fovea in Color Fundus Photographs. Med Image Anal. 13(6), 859–870 (2009)Google Scholar
  65. 65.
    Niemeijer, M., et al.: Automatic detection of red lesions in digital color fundus photographs. IEEE Med. Imaging 24(5), 584–592 (2005)Google Scholar
  66. 66.
    Spencer, T., et al.: An image- processing strategy for the segmentation and quantification in fluorescein angiograms of the ocular fundus. Comput. Biomed. Res. 29(4), 284–302 (1996)Google Scholar
  67. 67.
    Frame, A., et al.: A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms. Comput. Biol. Med. 28(3), 225–238 (1998)Google Scholar
  68. 68.
    Niemeijer, M., et al.: 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)Google Scholar
  69. 69.
    Abràmoff, M.D., et al.: 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), 193–8 (2008)Google Scholar
  70. 70.
    Abràmoff, M.D., Niemeijer, M.: The automatic detection of the optic disc location in retinal images using optic disc location regression. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4432–4435 (2006).  https://doi.org/10.1109/IEMBS.2006.259622
  71. 71.
    Niemeijer, M. et al.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Fitzpatrick, J.M., Sonka, M. (eds.) SPIE Medical Imaging, SPIE5370, pp. 648–656 (2004)Google Scholar
  72. 72.
    Niemeijer, M., Abràmoff, M.D., van Ginneken, B.: Information fusion for diabetic retinopathy CAD in digital color fundus photographs. IEEE Med. Imaging 28(5), 775–785 (2009)Google Scholar
  73. 73.
    Abràmoff, M.D., et al.: Automated early detection of diabetic retinopathy. Ophthalmology 117(6), 1147–1154 (2010)Google Scholar
  74. 74.
    Abràmoff, M.D., Suttorp-Schulten, M.S.: Web-based screening for diabetic retinopathy in a primary care population: the EyeCheck project. Telemed. J. E. Health 11(6), 668–674 (2005)Google Scholar
  75. 75.
    Fleming, A. D. et al.: Automated assessment of retinal image field of view. In: Proceedings of Medical Image Understanding and Analysis, pp. 129–132 (2004)Google Scholar
  76. 76.
    Hipwell, J.H., et al.: Automated detection of microaneurysms in digital red-free photographs: a diabetic retinopathy screening tool. Diabet. Med. 17(8), 588–594 (2000)Google Scholar
  77. 77.
    Cree, M.J., et al.: A Fully automated comparative microaneurysm digital detection system. Eye 11(Pt 5), 622–628 (1997)Google Scholar
  78. 78.
    Fleming, A.D., et al.: Automated microaneurysm detection using local contrast normalization and local vessel detection. IEEE Trans. Med. Imaging 25(9), 1223–1232 (2006)Google Scholar
  79. 79.
    Philip, S., et al.: The efficacy of automated ’disease/no disease’ grading for diabetic retinopathy in a systematic screening programme. Br. J. Ophthalmol. 91(11), 1512–1517 (2007)Google Scholar
  80. 80.
    Fleming, A.D., et al.: Automated grading for diabetic retinopathy: a large-scale audit using arbitration by clinical experts. Br. J. Ophthalmol. 94(12), 1606–10 (2010)Google Scholar
  81. 81.
    Barriga, E.S. et al.: Automatic system for diabetic retinopathy screening based on AM-FM, partial least squares, and support vector machines. In: Proceedings on IEEE International Symposium on Bimedical Imaging: From Nano to Macro 1349-1352 (2010)Google Scholar
  82. 82.
    Murray, V., et al.: Recent multiscale AM-FM methods in emerging applications in medical imaging. EURASIP J. Adv. Signal Process. 2012, 23 (2012)Google Scholar
  83. 83.
    Agurto, C.: Detection and phenotyping of retinal disease using AM-FM processing for feature extraction. In: The IEEE Proceedings of Asilomar Conference on Signals, Systems and Computers, pp. 659–663 (2008)Google Scholar
  84. 84.
    Murray, V., Rodriguez, P., Pattichis, M.S.: Multiscale AM-FM demodulation and image reconstruction methods with improved accuracy. IEEE Trans. Image Process. 19(5), 1138–1152 (2010)MathSciNetzbMATHGoogle Scholar
  85. 85.
    Agurto, C., et al.: Multi-scale AM-FM methods for diabetic retinopathy lesion detection. IEEE Trans. Med. Imaging 29(2), 502–512 (2010)Google Scholar
  86. 86.
    Agurto, C., et al.: Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images. Investig. Ophthalmol. Vis. Sci. 52(8), 5862–5871 (2011)Google Scholar
  87. 87.
    Dupas, B.: Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy. Diabetes Metab. 36(2), 213–220 (2010)Google Scholar
  88. 88.
    Walter, T., Klein, J. C.: Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques. In: Crespo, J., Maojo, V., Martin, F. (eds.) The Proceedings of the Second International Symposium on Medical Data Analysis, 2199, pp. 282–287. Springer, Berlin Heidelberg (2001)Google Scholar
  89. 89.
    Walter, T., et al.: Automatic detection of microaneurysms in color fundus images. Med. Image Anal. 11(6), 555–66 (2007)Google Scholar
  90. 90.
    Walter, T.: Application de la morphologie mathématique au diagnostic de la rétinopathie diabétique à partir d’images couleur,” In: Centre of Mathematical Morphology, Paris School of Mines Paris, defended September 12 (2003)Google Scholar
  91. 91.
    Walter, T., et al.: 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. Imaging 21(10), 1236–43 (2002)Google Scholar
  92. 92.
    Decencière, E., et al.: TeleOphta: machine learning and image processing methods for teleophthalmology. IRBM 34, 196–203 (2013)Google Scholar
  93. 93.
    Quellec, G., et al.: Automated assessment of diabetic retinopathy severity using content-based image retrieval in multimodal fundus photographs. Invest. Ophthalmol. Vis. Sci. 52(11), 8342–8348 (2011)Google Scholar
  94. 94.
    Quellec, G., et al.: A multiple-instance learning framework for diabetic retinopathy screening. Med. Image Anal. 16(6), 1228–1240 (2012)Google Scholar
  95. 95.
    Quellec, G., et al.: Case retrieval in medical databases by fusing heterogeneous information. IEEE Trans. Med. Imaging 30(1), 108–118 (2011)Google Scholar
  96. 96.
    Zhang, X. et al.: Application of the morphological ultimate opening to the detection of microaneurysms on eye fundus images from clinical databases. In: 13th International Congress of Stereology (ICS’13) (2011)Google Scholar
  97. 97.
    Zhang, X., et al.: Automatic detection of exudates in color retinal images. Investig. Ophthalmol. Vis. Sci. 53, 2083 (2012)Google Scholar
  98. 98.
    Zhang, X., et al.: Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med. Image Anal. 18, 1026–1043 (2014)Google Scholar
  99. 99.
    Quellec, G. et al.: Multimedia data mining for automatic diabetic retinopathy screening. In: Proceedings of 35th Annual International Conference of the IEEE EMBS, pp. 7144–7147 (2013)Google Scholar
  100. 100.
    Quellec, G., et al.: Wavelet optimization for content-based image retrieval in medical databases. Med. Image Anal. 14(2), 227–241 (2010)Google Scholar
  101. 101.
    Quellec, G. et al.: Weakly supervised classification of medical images. In: Proceedings IEEE International Symposium on Biomedical Imaging, pp. 110–113 (2012)Google Scholar
  102. 102.
    Quellec, G., et al.: Adaptive nonseparable wavelet transform via lifting and its application to content-based image retrieval. IEEE Trans. Image Process. 19(1), 25–35 (2010)MathSciNetzbMATHGoogle Scholar
  103. 103.
    Reza, A.W., Eswaran, C.: A decision support system for automatic screening of non-proliferative diabetic retinopathy. J. Med. Syst. 35(1), 17–24 (2011)Google Scholar
  104. 104.
    Reza, A.W., Eswaran, C., Hati, S.: Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds. J. Med. Syst. 33(1), 73–80 (2009)Google Scholar
  105. 105.
    Yen, G.G., Leong, W.F.: A sorting system for hierarchical grading of diabetic fundus images: a preliminary study. IEEE Trans. Inf. Technol. Biomed. 12(1), 118–130 (2008)Google Scholar
  106. 106.
    Joshi, G.D., Sivaswamy, J.: DrishtiCare: a telescreening platform for diabetic retinopathy powered with fundus image analysis. J. Diabetes Sci. Technol. 5(1), 1–9 (2011)Google Scholar
  107. 107.
    Abràmoff, M.D., et al.: 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), 193–8 (2008)Google Scholar
  108. 108.
    Jelinek, H.F., Cree, M.J. (eds.): Automated image detection of retinal pathology. CRC Press, Boca Raton (2010)Google Scholar
  109. 109.
    Usher, D.: Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet. Med. 21(1), 84–90 (2004)Google Scholar
  110. 110.
    Roychowdhury, S., Koozekanani, D.D., Parhi, K.K.: DREAM: diabetic retinopathy analysis using Machine learning. IEEE J. Biomed. Health Inform. 18(5), 1717–1728 (2014)Google Scholar
  111. 111.
    Fadzil, M.H.A., Izhar, L.I., Nugroho, H., Nugroho, H.A.: Analysis of retinal fundus images for grading of diabetic retinopathy severity. Med. Biol. Eng. Comput. 49, 693–700 (2011)Google Scholar
  112. 112.
    Alipour, S.H.M., Rabbani, H., Akhlaghi, M.R.: Diabetic retinopathy grading by digital curvelet transform. Comput. Math. Methods Med. 2012, 761901 (2012)zbMATHGoogle Scholar
  113. 113.
    Mookiah, M.R.K., et al.: Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowl Based Syst 39, 9–22 (2013)Google Scholar
  114. 114.
    Rocha, A., et al.: Points of interest and visual dictionaries for automatic retinal lesion detection. IEEE Trans. Biomed. Eng. 59(8), 2244–2253 (2012)Google Scholar
  115. 115.
    Jelinek, H. et al.: Data fusion for multi-lesion diabetic retinopathy detection. In: Proceedings of IEEE Computer-Based Medical System, pp. 1–4 (2012)Google Scholar
  116. 116.
    Pires, R., et al.: Assessing the need for referral in automatic diabetic retinopathy detection. IEEE Trans. Biomed. Eng. 60(12), 3391–3398 (2013)Google Scholar
  117. 117.
    Pires, R., et al.: Automatic diabetic retinopathy detection using BossaNova representation. Proc. IEEE Eng. Med. Biol. Soc. 2014, 146–9 (2014)Google Scholar
  118. 118.
    Colas, E. et al.: Deep learning approach for diabetic retinopathy screening. In: Proceedings of the 2016 European Association for Vision and Eye Research Conference (EVER), Nice, France (2016)Google Scholar
  119. 119.
    Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)Google Scholar
  120. 120.
    Szegedy, C. et al.: Rethinking the Inception Architecture for Computer Vision. December 2015. http://arxiv.org/pdf/1512.00567v3.pdf (2015). Accessed Mar 2018
  121. 121.
    Ting, D., et al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22), 2211–2223 (2017)Google Scholar
  122. 122.
    Gargeya, R., Leng, T.: Automated identification of diabetic retinopathy using deep learning. Ophthalmology 124(7), 962–969 (2017)Google Scholar
  123. 123.
    Abràmoff, M.D., et al.: Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investig. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016)Google Scholar
  124. 124.
    Quellec, G., et al.: Deep image mining for diabetic retinopathy screening. Med. Image Anal. 39, 178–193 (2017)Google Scholar
  125. 125.
  126. 126.
    Zhou, L., et al.: Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images. IET Image Process. 12(4), 563–571 (2017)Google Scholar
  127. 127.
    Costa, P., et al.: A weakly- supervised framework for interpretable diabetic retinopathy detection on retinal images. IEEE Access. 6, 18747–18758 (2018)Google Scholar
  128. 128.
    Gondal, W. M. et al.: Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 2069–2073 (2017)Google Scholar
  129. 129.
    Abràmoff, M.D., et al.: Automated analysis of retinal images for detection of referable diabetic retinopathy. JAMA Ophthalmol. 131(3), 351–7 (2013)Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.The Australian e-Health Research Centre, Health & Biosecurity Flagship, CSIROFloreatAustralia
  2. 2.iHealthScreen Inc.Richmond HillUSA
  3. 3.The Royal Perth HospitalPerthAustralia

Personalised recommendations