Journal of Medical Systems

, Volume 36, Issue 3, pp 1997–2004 | Cite as

Automated Identification of Exudates and Optic Disc Based on Inverse Surface Thresholding

ORIGINAL PAPER

Abstract

This paper presents a new approach to detect exudates and optic disc from color fundus images based on inverse surface thresholding. The strategy involves the applications of fuzzy c-means clustering, edge detection, otsu thresholding and inverse surface thresholding. The main advantage of the proposed approach is that it does not depend on manually selected parameters that are normally chosen to suit the tested databases. When applied to two sets of databases the proposed method outperforms a method based on watershed segmentation.

Keywords

Diabetic retinopathy Exudates Biomedical applications Inverse surface thresholding 

References

  1. 1.
    Sinthanayothin, C., Boyce, J. F., Cook, H. L., and Wiliamson, 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.CrossRefGoogle Scholar
  2. 2.
    Lu, S., and Lim, J. W., Automatic optic disc detection from retinal images by line operator. IEEE Trans. Biomed. Eng. 58(1):88–94, 2011.CrossRefGoogle Scholar
  3. 3.
    Abdel-Ghafar, R. A, Morris, T., Ritchings, T., Wood, T., Detection and characteristic of the optic disc in glaucoma and diabetic retinopathy. In Proc. Medical Image Understanding Analysis Conf, London, UK, 2004.Google Scholar
  4. 4.
    Osareh, A., Automated identification of diabetic retinal exudates and the optic disc. Ph.D. dissertation, Department of Computer Science, Faculty of Engineering, University of Bristol, Bristol, UK, 2004.Google Scholar
  5. 5.
    Hoover, A., and Goldbaum, M., Locating optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imag. 22(8):951–958, 2003.CrossRefGoogle Scholar
  6. 6.
    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 96:80–86, 1989.Google Scholar
  7. 7.
    Philips, R., Forrester, J., and Sharp, P., Automated detection and quantification of retinal exudates. Graefe Arch. Clin. Exp. Ophthalmol. 231(2):90–94, 1994.CrossRefGoogle Scholar
  8. 8.
    Frame, A. J., Undill, P. E., Cree, M. J., Olson, J. A., McHardy, K. C., Sharp, P. F., and Forrester, J. F., A comparison of computer based classification methods applied to the detection of microaneurysms in ophtalmic fluorescein angiograms. Comput. Biol. Med. 28:225–238, 1998.CrossRefGoogle Scholar
  9. 9.
    Wang, H., Hsu, W., Goh, K. G., and Lee, M. L., An effective approach to detect lesions in color retinal images. Proceedings of IEEE Conference on Computer Vision and Pattern recognition, Hilton Head Island, USA, pp. 181–186, 2000.Google Scholar
  10. 10.
    Ege, B. M., Hejlesen, O. K., Larsen, O. V., Moller, K., Jennings, B., Kerr, D., and Cavan, D. A., Screening for diabetic retinopathy using computer based image analysis and statistical classification. Comput. Meth. Programs Biomed. 62(3):165–175, 2000.CrossRefGoogle Scholar
  11. 11.
    Osareh, A., Mirmehdi, M., Thomas, B., and Markham, R., Automatic recognition of exudative maculopathy using fuzzy c-means clustering and neural networks. In Proc. Medical Image Understanding Analysis Conf., pp. 49–52, July 2001.Google Scholar
  12. 12.
    Walter, T. J., Klein, 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.CrossRefGoogle Scholar
  13. 13.
    Kevin, N., Nayak, J., Bhat, S. N., Enhancement of retinal fundus Image to highlight the features for detection of abnormal eyes. TENCON 2006. 2006 IEEE Region 10 Conference, pp. 1–4, 2006.Google Scholar
  14. 14.
    Li, H., and Chutatape, O., A model-based approach for automated feature extraction in fundus images. ICCV 2003:394–399, 2003.Google Scholar
  15. 15.
    Sanchez, C. I., Hornero, R., Lopez, M. I., and Poza, J., Retinal image analysis to detect and quantify lesions associated with diabetic retinopathy. Proc. 26th IEEE Annual International Conf. on Engineering in Medicine and Biology Society (EMBC) 3:1624–1627, 2004.Google Scholar
  16. 16.
    Chuai-Aree, S., Lursinsap, C., Sophatsathit, P., and Siripant, S., Fuzzy C-mean: A statistical feature classification of text and image segmentation method. Proc. of Intern. Conf. on Intelligent Technology 2000, December 13–15, Assumption University Bangkok, Thailand, pp. 279–284, 2000.Google Scholar
  17. 17.
    Gonzalez, R. C., and Woods, R. E., Digital image processing. Prentice Hall, Upper Saddle River, 2002.Google Scholar
  18. 18.
    Otsu, N., A threshold selection method from gray-level histogram. IEEE Trans. Syst. Man Cybern. 9(1):62–66, 1979.MathSciNetCrossRefGoogle Scholar
  19. 19.
    Reza, A. W., Eswaran, C., and Hati, S., Automatic tracing of optic disc and exudates from color fundus images using fixed and variable threshold. J. Med. Syst. 33:73–80, 2009.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Electrical Engineering Department, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  2. 2.School of Mechatronic EngineeringUniversity Malaysia PerlisKangarMalaysia
  3. 3.National University Hospital of MalaysiaKuala LumpurMalaysia

Personalised recommendations