Skip to main content

Advertisement

SpringerLink
Log in

Automated Detection of Dark and Bright Lesions in Retinal Images for Early Detection of Diabetic Retinopathy

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

There is an ever-increasing interest in the development of automatic medical diagnosis systems due to the advancement in computing technology and also to improve the service by medical community. The knowledge about health and disease is required for reliable and accurate medical diagnosis. Diabetic Retinopathy (DR) is one of the most common causes of blindness and it can be prevented if detected and treated early. DR has different signs and the most distinctive are microaneurysm and haemorrhage which are dark lesions and hard exudates and cotton wool spots which are bright lesions. Location and structure of blood vessels and optic disk play important role in accurate detection and classification of dark and bright lesions for early detection of DR. In this article, we propose a computer aided system for the early detection of DR. The article presents algorithms for retinal image preprocessing, blood vessel enhancement and segmentation and optic disk localization and detection which eventually lead to detection of different DR lesions using proposed hybrid fuzzy classifier. The developed methods are tested on four different publicly available databases. The presented methods are compared with recently published methods and the results show that presented methods outperform all others.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Amos, A. F., McCarty, D. J., and Zimmet, P., The rising global burden of diabetes and its complications: Estimates and projections to the year 2010. Diabet. Med., 14:S1–S85, 1997.

    Article  Google Scholar 

  2. Kohner, E. M., Aldington, S. J., Stratton, I. M., Manley, S. E., Holman, R. R., Matthews, D. R., et al., United Kingdom prospective diabetes study, 30: Diabetic retinopathy at diagnosis of noninsulin-dependent diabetes mellitus and associated risk factors. Arch. Ophthalmol. 116(3):297–303, 1998.

    Google Scholar 

  3. Molven, A., Ringdal, M., Nordbo, A. M., Raeder, H., Stoy, J., Lipkind, G. M., et al., Mutations in the insulin gene can cause MODY and autoantibody-negative type 1 diabetes. Diabetes 57(4):1131–1135, 2008.

    Article  Google Scholar 

  4. Lee, S. C., Lee, E. T., Kingsley, R. M., Wang, Y., Russell, D., Klein, R., and Wanr, A., Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts. Graefes Arch. Clin. Exp. Ophtalmol. 119(4):509–515, 2001.

    Article  Google Scholar 

  5. 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. Diabetes UK. Diabet. Med. 21(1): 84–90, 2003.

    Article  Google Scholar 

  6. Sinthanayothin, C., Kongbunkiat, V., Phoojaruenchanachain, S., and Singlavanija, A., Automated screening system for diabetic retinopathy. In: Proc. of the 3rd International Symposium on Image and Signal Processing and Analysis. pp. 915–920, 2003.

  7. Osareh, A., Mirmehdi, M., Thomas, B., and Markham, R., Classification and localisation of diabeticrelated eye disease. In: Proc. 7th European Conference on Computer Vision. LNCS. Vol. 2353, pp. 502–516. Berlin, Heidelberg: Springer, 2002.

    Google Scholar 

  8. Zhou, L., Rzeszotarski, M. S., Singerman, L. J., and Chokreff, J. M., The detection and quantification of retinopathy using digital angiograms. IEEE Trans. Med. Imag. 13(4):619–626, 1994.

    Article  Google Scholar 

  9. Soares, J. V. B., Leandro, J. J. G., Cesar, R. M., Jelinek, H. F., and Cree, M. J., Retinal vessel segmentation using the 2-D gabor wavelet and supervised classification. IEEE Trans. Med. Imag. 25(9):1214–1222, 2006.

    Article  Google Scholar 

  10. Nayak, J., Subbanna Bhat, P., Rajendra Acharya, U., Lim, C. M., and Kagathi, M., Automated identification of diabetic retinopathy stages using digital fundus images. J. Med. Syst. 32:107–115, 2008.

    Article  Google Scholar 

  11. Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., and van Ginneken, B., Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imag. 23(4):501–509, 2004.

    Article  Google Scholar 

  12. 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.

    Article  Google Scholar 

  13. Reza, A. W., Eswaran, C., and Dimyati, K., Diagnosis of diabetic retinopathy: Automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation. J. Med. Syst. 2010. doi:10.1007/s10916-009-9426-y

  14. Narasimha-Iyer, H., Can, A., Roysam, B., Stewart, C. V., Tanenbaum, H. L., Majerovics, A., and Singh, H., Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy. IEEE Trans. Biomed. Eng. 53(6):1084–1098, 2006.

    Article  Google Scholar 

  15. Sinthanayothin, C., Boyce, J. A., Cook, H. L., and Williamson, T. H., Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br. J. Ophthalmol. 83:902–910, 1999.

    Article  Google Scholar 

  16. Hoover, A., Goldbaum, M., Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imag. 22(8):951–958, 2003.

    Article  Google Scholar 

  17. Tariq, A., and Akram, M. U., An automated system for colored retinal image background and noise segmentation. In: IEEE Symposium on Industrial Electronics and Applications (ISIEA 2010). pp. 405–409, 2010.

  18. Vasilevski, A., and Siddiqi, K., Flux maximizing geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 24(12):1565–1578, 2002.

    Article  Google Scholar 

  19. Jiang, X., and Mojon, D.: Adaptive local thresholding by verificationbased multithreshold probing with application to vessel detection in retinal images. IEEE Trans. Pattern Anal. Mach. Intell. 25(1):131–137, 2003.

    Article  Google Scholar 

  20. Foracchia, M., Grisam, E., and Ruggeri, A., Detection of the optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Trans. Med. Imag. 23(10):1189–1195, 2004.

    Article  Google Scholar 

  21. Li, H., and Chutatape, O., Automated feature extraction in color retinal images by a model based approach. IEEE Trans. Biomed. Eng. 51(2):246–254, 2004.

    Article  Google Scholar 

  22. Mendona, A. M., Campilho, A. J., Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imag. 25(9):1200–1213, 2006.

    Article  Google Scholar 

  23. Antoine, J. P., Carette, P., Murenzi, R., and Piette, B., Image analysis with two-dimensional continuous wavelet transform. Signal Process. 31(3):241–272, 1993.

    Article  MATH  Google Scholar 

  24. Gonzalez, R. C., and Woods, R. E., Digital image processing. Second edition. Englewood Cliffs, NJ: Prentice Hall, 2002.

    Google Scholar 

  25. Sekhar, S., Al-Nuaimy, W., Nandi, A. K., Automated localisation of retinal optic disk using Hough transform, In: 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro. pp. 1577–1580, 14–17 May 2008.

  26. Kauppi, T., Kalesnykiene, V., Kamarainen, J. K., Lensu, L., Sorri, I., Uusitalo, H., Kälviäinen, H., Pietilä, J., DIARETDB0: Evaluation database and methodology for diabetic retinopathy algorithms. Technical Report, 2006.

  27. Kauppi, T., Kalesnykiene, V., Kamarainen, J.-K., Lensu, L., Sorri, I., Raninen A., Voutilainen R., Uusitalo, H., Kälviäinen, H., Pietilä, J., DIARETDB1 diabetic retinopathy database and evaluation protocol, Technical report, 2007.

  28. 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(10):1236–1243, 2002.

    Article  Google Scholar 

  29. Akram, M. U., Khan, A., Iqbal, K., and Butt, W. H., Retinal image: Optic disk localization and detection. In: Image Analysis and Recognition, Lecture Notes in Computer Science, LNCS 6112, Portugal. pp. 40–49, Berlin, Heidelberg: Springer, 2010.

    Google Scholar 

  30. Niemeijer, M., Abramoff, M. D., and Ginneken, B. V., Information fusion for diabetic retinopathy CAD in digital color fundus photographs. IEEE Trans. Med. Imag. 28(5):775–785, 2009.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Hoover et al. [16] and Staal et al. [11] for making their databases publicly available.

Author information

Authors and Affiliations

  1. Department of Computer Engineering, College of Electrical & Mechanical Engineering, National University of Sciences and Technology, Rawalpindi, Pakistan

    Usman M. Akram & Shoab A. Khan

Authors
  1. Usman M. Akram
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shoab A. Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Usman M. Akram.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Akram, U.M., Khan, S.A. Automated Detection of Dark and Bright Lesions in Retinal Images for Early Detection of Diabetic Retinopathy. J Med Syst 36, 3151–3162 (2012). https://doi.org/10.1007/s10916-011-9802-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-011-9802-2

Keywords

Search

Navigation