Automated Screening of Diabetic Retinopathy through Advanced Image Processing Techniques

Shaji C1, V. Betcy Thanga Shoba2, Jovin R. B3* and Arockia Rajasekar4
1Assistant Professor, Department of Computer Science, Arunachala Arts and Science (Women) College, Vellichanthai, Affiliated to Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India.
2Assistant Professor, Department of Computer Science, Government Arts and Science College, Konam, Nagercoil, Affiliated to Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu, India.
3Research Scholar, Department of Commerce, St. Joseph’s College (Autonomous), Tiruchirappalli, Affiliated to Bharathidasan University, Tiruchirappalli, Tamil Nadu, India.
4Assistant Professor, Department of Commerce, St. Joseph’s College (Autonomous), Tiruchirappalli, Affiliated to Bharathidasan University, Tiruchirappalli, Tamil Nadu, India.
*Corresponding Author E-mail: shobaedwindec27@gmail.com
Keywords: Diabetic retinopathy; retinal image processing; image analysis; optic disc localization; blood vessel segmentation
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Abstract

Diabetic retinopathy is a retinal vascular disease resulting from fluid leakage in damaged blood vessels. Early diagnosis is essential and can be supported by automated screening systems that rely on reliable image analysis techniques. This study proposes an exudate detection approach employing both coarse and fine segmentation. The coarse stage applies a local variation operator to extract candidate regions with well-defined edges. Validation against clinician-labelled ground truth yielded a sensitivity of 89.7%, specificity of 99.3%, and accuracy of 99.4% on a retinal image dataset, indicating robustness to variations in image quality.

Citation

Shaji C, V. Betcy Thanga Shoba, Jovin R. B & Arockia Rajasekar, “Automated Screening of Diabetic Retinopathy through Advanced Image Processing Techniques”, International Journal of Current Science Research (IJCSR) e-ISSN: 2454-5422: 12(2) 2026: 1-10

DOI: https://doi.org/10.5281/zenodo.19148666

License

© 2026 The Author(s). Published by Dr. BGR Publications . The authors retain copyright of this article. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

References

  1. S. Wild, G. Roglic, A. Green, R. Sicree, and H. King, “Global prevalence of diabetes: estimates for the year 2000 and projections for 2030,” Diabetes Care, vol. 27, no. 5, pp. 1047–1053, 2004.
  2. S. J. Lee, C. A. McCarty, H. R. Taylor, and J. E. Keeffe, “Costs of mobile screening for diabetic retinopathy: a practical framework for rural populations,” Aust J Rural Health, vol. 9, no. 4, pp. 186–192, 2001.
  3. C. A. McCarty, C. W. Lloyd-Smith, S. E. Lee, P. M. Livingston, Y. L. Stanislavsky, and H. R. Taylor, “Use of eye care services by people with diabetes: the Melbourne Visual Impairment Project,” Br J Ophthalmol, vol. 82, no. 4, pp. 410–414, 1998.
  4. D. A. Askew, L. Crossland, R. S. Ware, S. Begg, P. Cranstoun, P. Mitchell, and C. L. Jackson, “Diabetic retinopathy screening and monitoring of early stage disease in general practice: design and methods,” Contemp Clin Trials, vol. 33, no. 5, pp. 969–975, 2012.
  5. H. C. Looker, S. O. Nyangoma, D. Cromie, J. A. Olson, G. P. Leese, M. Black, J. Doig, N. Lee, R. S. Lindsay, J. A. McKnight, A. D. Morris, S. Philip, N. Sattar, S. H. Wild, and H. M. Colhoun, “Diabetic retinopathy at diagnosis of type 2 diabetes in Scotland,” Diabetologia, vol. 55, no. 9, pp. 2335–2342, 2012.
  6. T. Peto and C. Tadros, “Screening for diabetic retinopathy and diabetic macular edema in the United Kingdom,” Curr. Diab. Rep., vol. 12, no. 4, pp. 338–345, 2012.
  7. R. C. Gonzalez and E. Richard, “Woods, digital image processing,” ed: Prentice Hall Press, ISBN 0-201-18075-8, 2002.
  8. M. J. Cree, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester, “The preprocessing of retinal images for the detection of fluorescein leakage,” Physics in medicine and biology, vol. 44, no. 1, p. 293, 1999.
  9. M. Foracchia, E. Grisan, and A. Ruggeri, “Detection of optic disc in retinal images by means of a geometrical model of vessel structure,” Medical Imaging, IEEE Transactions on, vol. 23, no. 10, pp. 1189–1195, 2004.
  10. T. Spencer, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester, “An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus,” Computers and biomedical research, vol. 29, no. 4, pp. 284–302, 1996.
  11. T. Walter, J.-C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudates in color fundus images of the human retina,” Medical Imaging, IEEE Transactions on, vol. 21, no. 10, pp. 1236–1243, 2002.
  12. Y. Wang, W. Tan, and S. C. Lee, “Illumination normalization of retinal images using sampling and interpolation,” in Medical Imaging 2001. International Society for Optics and Photonics, 2001, pp. 500–507.
  13. M. Foracchia, E. Grisan, and A. Ruggeri, “Luminosity and contrast normalization in retinal images,” Medical Image Analysis, vol. 9, no. 3, pp. 179–190, 2005.
  14. D. S. Shin, N. B. Javornik, and J. W. Berger, “Computer-assisted, interactive fundus image processing for macular drusen quantitation,” Ophthalmology, vol. 106, no. 6, pp. 1119–1125, 1999.
  15. J. J. Leandro, J. V. Soares, R. M. Cesar Jr, and H. F. Jelinek, “Blood vessels segmentation in nonmydriatic images using wavelets and statistical classifiers,” in Computer Graphics and Image Processing, 2003. SIBGRAPI 2003. XVI Brazilian Symposium on. IEEE, 2003, pp. 262–269.
  16. K. Rapantzikos, M. Zervakis, and K. Balas, “Detection and segmentation of drusen deposits on human retina: Potential in the diagnosis of age-related macular degeneration,” Medical image analysis, vol. 7, no. 1, pp. 95–108, 2003.
  17. C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, “Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images,” British Journal of Ophthalmology, vol. 83, no. 8, pp. 902–910, 1999.
  18. J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, and L. Kennedy, “Optic nerve head segmentation,” Medical Imaging, IEEE Transactions on, vol. 23, no. 2, pp. 256–264, 2004.
  19. D. Usher, M. Dumskyj, M. Himaga, T. Williamson, S. Nussey, and J. Boyce, “Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening,” Diabetic Medicine, vol. 21, no. 1, pp. 84–90, 2004.
  20. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two dimensional matched filters,” IEEE Transactions on medical imaging, vol. 8, no. 3, pp. 263–269, 1989.
  21. M. Yulong and X. Dingru, “Recognizing the glaucoma from ocular fundus image by image analysts,” in Engineering in Medicine and Biology Society, 1990., Proceedings of the Twelfth Annual International Conference of the IEEE. IEEE, 1990, pp. 178–179.
  22. A. Pinz, S. Bernogger, P. Datlinger, and A. Kruger, “Mapping the human retina,” Medical Imaging, IEEE Transactions on, vol. 17, no. 4, pp. 606–619, 1998.
  23. Z. Liu, C. Opas, and S. M. Krishnan, “Automatic image analysis of fundus photograph,” in Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE, vol. 2. IEEE, 1997, pp. 524–525.
  24. B. Kochner, D. Schuhmann, M. Michaelis, G. Mann, and K.-H. Englmeier, “Course tracking and contour extraction of retinal vessels from color fundus photographs: Most efficient use of steerable filters for model-based image analysis,” in Proceedings of the SPIE Conference on Medical Imaging, vol. 3338, 1998 , pp. 755–761.
  25. B. M. Ege, O. K. Hejlesen, O. V. Larsen, K. Møller, B. Jennings, D. Kerr, and D. A. Cavan, “Screening for diabetic retinopathy using computer based image analysis and statistical classification,” Computer methods and programs in biomedicine, vol. 62, no. 3, pp. 165–175, 2000.
  26. C. I. Sanchez,´ R. Hornero, M. I. Lopez, and J. Poza, “Retinal image analysis to detect and quantify lesions associated with diabetic retinopathy,” in Engineering in Medicine and Biology Society, 2004. IEMBS’04. 26th Annual International Conference of the IEEE, vol. 1. IEEE, 2004, pp. 1624–1627.
  27. M. Lalonde, M. Beaulieu, and L. Gagnon, “Fast and robust optic disc detection using pyramidal decomposition and hausdorff-based template matching,” Medical Imaging, IEEE Transactions on, vol. 20, no. 11, pp. 1193–1200, 2001.
  28. K. Akita and H. Kuga, “A computer method of understanding ocular fundus images,” Pattern recognition, vol. 15, no. 6, pp. 431–443, 1982.
  29. A. Hoover and M. Goldbaum, “Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels,” Medical Imaging, IEEE Transactions on, vol. 22, no. 8, pp. 951–958, 2003.
  30. T. Walter and J.-C. Klein, “Segmentation of color fundus images of the human retina: Detection of the optic disc and the vascular tree using morphological techniques,” in Medical Data Analysis. Springer, 2001, pp. 282–287.
  31. A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, “Colour morphology and snakes for optic disc localisation,” in The 6th medical image understanding and analysis conference. BMVA Press, 2002, pp. 21–24.
  32. B. Zhang, L. Zhang, L. Zhang, and F. Karray, “Retinal vessel extraction by matched filter with first-order derivative of gaussian,” Computers in biology and medicine, vol. 40, no. 4, pp. 438–445, 2010.
  33. J. Odstrcilík, J. Jan, J. Gazarek, and R. Kolař, “Improvement of vessel segmentation by matched filtering in colour retinal images,” in World Congress on Medical Physics and Biomedical Engineering, September 7-12, 2009, Munich, Germany. Springer, 2009, pp. 327–330.
  34. F. Zana and J.-C. Klein, “A multimodal registration algorithm of eye fundus images using vessels detection and hough transform,” Medical Imaging, IEEE Transactions on, vol. 18, no. 5, pp. 419–428, 1999.
  35. H. Wang, W. Hsu, K. G. Goh, and M. L. Lee, “An effective approach to detect lesions in color retinal images,” in Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on, vol. 2. IEEE, 2000, pp. 181–186.
  36. M. Niemeijer, M. Abramoff, and B. van Ginneken, “Automatic detection of the presence of bright lesions in color fundus photographs,” Proceedings of IFMBE the 3rd European Medical and Biological Engineering Conference, vol. 11, pp. 1823–2839 , 2005.
  37. M. Niemeijer, B. van Ginneken, S. R. Russell, M. S. Suttorp-Schulten, and M. D. Abramoff, “Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis,” Investigative ophthalmology & visual science, vol. 48, no. 5, pp. 2260–2267, 2007.
  38. C. I. Sanchez, R. Hornero, M. I. Lopez, M. Aboy, J. Poza, and D. Abasolo, “A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis,” Medical Engineering & Physics, vol. 30, no. 3, pp. 350–357 , 2008.
  39. X. Zhang and O. Chutatape, “Top-down and bottom-up strategies in lesion detection of background diabetic retinopathy,” in Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 2. IEEE, 2005, pp. 422–428.
  40. S. S. A. Hassan, D. B. Bong, and M. Premsenthil, “Detection of neovascularization in diabetic retinopathy,” Journal of digital imaging, vol. 25, no. 3, pp. 437–444, 2012.
  41. M. Niemeijer, J. J. Staal, M. Ginneken B. v., Loog, and M. D. Abramoff. (2004) DRIVE: digital retinal images for vessel extraction. [Online]. Available: http://www.isi.uu.nl/Research/Databases/DRIVE

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