Skip to main content
SpringerLink
Log in

Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages

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

Abstract

Diabetic retinopathy (DR) is a condition where the retina is damaged due to fluid leaking from the blood vessels into the retina. In extreme cases, the patient will become blind. Therefore, early detection of diabetic retinopathy is crucial to prevent blindness. Various image processing techniques have been used to identify the different stages of diabetes retinopathy. The application of non-linear features of the higher-order spectra (HOS) was found to be efficient as it is more suitable for the detection of shapes. The aim of this work is to automatically identify the normal, mild DR, moderate DR, severe DR and prolific DR. The parameters are extracted from the raw images using the HOS techniques and fed to the support vector machine (SVM) classifier. This paper presents classification of five kinds of eye classes using SVM classifier. Our protocol uses, 300 subjects consisting of five different kinds of eye disease conditions. We demonstrate a sensitivity of 82% for the classifier with the specificity of 88%.

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

Similar content being viewed by others

References

  1. Acharya, U. R., Ng, E. Y. K., and Suri, J. S., Image modelling of human eye. Artech House, Norwood, 2008April.

    Google Scholar 

  2. Burgess, C. J., A tutorial on support vector machines for pattern recognition. Data. Min. Knowl. Discov. 2:21–47, 1998.

    Article  Google Scholar 

  3. Chandran, V., Carswell, B., Boashash, B., and Elgar, S. L., Pattern Recognition Using Invariants Defined from Higher Order Spectra: 2-D Image Inputs. IEEE Transcations on image processing. 6:703–712, 1997.

    Article  Google Scholar 

  4. Christianini, N., and Taylor, J., Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge, 2000.

    Google Scholar 

  5. Chua, K. C., Chandran, V., Acharya, U. R., and Lim, C. M., Analysis of epileptic EEG signals using higher order spectra. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1:6495–6498, 2007.

    Google Scholar 

  6. Chua, K. C., Chandran, V., Acharya, U. R., and Lim, C. M., Cardiac State Diagnosis Using Higher Order Spectra of Heart Rate Variability. J. Med. Eng. Technol. 32:2145–155, 2006.

    Article  Google Scholar 

  7. Cigna healthcare coverage position- A Report, 2007. Accessed at http://www.cigna.com/customer_care/healthcare_professional/coverage_positions/medical/mm_0080_coveragepositioncriteria_imaging_systems_optical.pdf, 5th December 2007.

  8. Cree, M. J., Leandro, J. J. G., Soares, J. V. B., Cesar, R. M., 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, Australia, January 30–February 4, 2005.

  9. V. David Sanchez, A., Advanced support vector machines and kernel methods. Neurocomputing. 55:1, 25–20, 2003.

    Article  Google Scholar 

  10. 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. 26:1226–229, 2003.

    Article  Google Scholar 

  11. Forracchia, M., Grisan, M. E., and Ruggeri, A., Extraction and quantitative description of vessel features in hypertensive retinopathy fundus images, presented at CAFIA2001. University of Padua, Padua, 2001.

    Google Scholar 

  12. Frank, R. N., Diabetic retinopathy. Prog. Retin. Eye Res. 14:2361–392, 1995.

    Article  Google Scholar 

  13. Gonzalez, R. C., and Wintz, P., Digital image processing, 2nd edition. Addison-Wesley, Reading, 1987.

    Google Scholar 

  14. Hayashi J, Takamitsu Kunieda, Joshua Cole, Ryusuke Soga, Yuji Hatanaka, Miao Lu, Takeshi Hara and Hiroshi Fujita: “A development of computer-aided diagnosis system using fundus images”. Proceeding of the 7th International Conference on Virtual Systems and MultiMedia (VSMM 2001), 429–438, 2001.

  15. Hsu, C. W., and Lin, C. J., A comparison of methods for multi-class support vector machines. IEEE Trans. on Neural Networks. 13:415–425, 2002.

    Article  Google Scholar 

  16. Liverpool Declaration, Screening for Diabetic Retinopathy in Europe 15 years after the St. Vincent Declaration. Accessed at http://reseau-ophdiat.aphp.fr/Document/Doc/confliverpool.pdf, 20th December 2007.

  17. Kahai, P., Namuduri, K. R., and Thompson, H., A decision support framework for automated screening of diabetic retinopathy. Int J Biomed Imaging. 1:1–8, 2006.

    Article  Google Scholar 

  18. Lei, H., and Govindaraju, V., Half-Against-Half Multi-class Support Vector Machines. Proceeding Sixth International Workshop on Multiple Classifier Systems. Springer, Berlin, pp. 156–164, 2005.

    Google Scholar 

  19. 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.

    Article  Google Scholar 

  20. Muller, K. R., Mika, S., Ratsch, G., Tsuda, K., and Scholkopf, B., An introduction to Kernel Based Learning Algorithms. IEEE Trans. Neural Netw. 12:2181–201, 2001.

    Article  Google Scholar 

  21. 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. 32:2107–115, 2008.

    Article  Google Scholar 

  22. Nikias, C. L., and Petropulu, A. P., Higher-order spectra analysis: a nonlinear signal processing framework. Prentice Hall, Englewood Cliffs, 1993.

    MATH  Google Scholar 

  23. Nicolai, L., Jannik, G., Michael, G., Henrik, L. A., and Michael, L., Automated detection of diabetic retinopathy in a fundus photographic screening population. Investig. Ophthalmol. Vis. Sci. 44:767–771, 2003.

    Article  Google Scholar 

  24. Niemeijer, M., van Ginneken, B., Staal, J., Suttorp-Schulten, M., and Abr’amoff, M., Automatic detection of red lesions in digital color fundus photographs. IEEE Trans. Med. Imag. 24:5584–592, 2005.

    Article  Google Scholar 

  25. 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. 137:3445–452, 2004.

    Article  Google Scholar 

  26. Platt, C. J., Chrisianini, N., and Shawe-Taylor, J., Large Margin DAGs for multiclass classification. Adv. Neural Inf. Process. Syst. 12:547–553, 2000.

    Google Scholar 

  27. 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.

    Article  Google Scholar 

  28. Shao, Y., and Celenk, M., Higher-order spectra (HOS) invariants for shape recognition. Pattern Recogn. 34:112097–2113, 2001.

    Article  MATH  Google Scholar 

  29. Tan, T. G., Acharya, U. R.,Ng, E. Y. K., Automated identification of eye diseases using higher order spectra. J. Mech. Med. Biol., 8(1):121–136, 2008.

    Article  Google Scholar 

  30. Vallabha, D., Dorairaj, R., Namuduri K. R., and Thompson, H., Automated Detection and Classification of Vascular Abnormalities in Diabetic Retinopathy. 38th Asilomar Conference on Signals, Systems and Computers, November 2004.

  31. Vapnik, V., Statistical learning theory. Wiley, New York, 1998.

    MATH  Google Scholar 

  32. Wang, H., Hsu, W., Goh, K., and Lee, M., An effective approach to detect lesions in colour retinal images. Proc. IEEE Conf. Comp. Vis. Pattern Recognit. 2:181–187, 2000.

    Google Scholar 

  33. Weston, J., and Watkins, C., “Multi-class support vector machines Technical Report” CSD-TR-98-04. Department of Computer Science, Royal Holloway, University of London, Egham, 1998.

    Google Scholar 

  34. 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. Inf. Sci. 178:106–121, 2008.

    Article  Google Scholar 

  35. Xiaohui, Z., and Chutatape, O., Detection and classification of bright lesions in colour fundus Images. Int. Conf. Image Process. 1:139–142, 2004.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of ECE, Ngee Ann Polytechnic, Singapore, Singapore

    Rajendra Acharya U & Chua Kuang Chua

  2. Graduate School of Medical System Engineering, Chiba University, Chiba, 263–8522, Japan

    Wenwei Yu

  3. Eye Centre, National University Hospital, Singapore, Singapore

    Caroline Chee

  4. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Nanyang, Singapore

    E. Y. K. Ng

Authors
  1. Rajendra Acharya U
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chua Kuang Chua
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. Y. K. Ng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenwei Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Caroline Chee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajendra Acharya U.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Acharya U, R., Chua, C.K., Ng, E.Y.K. et al. Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages. J Med Syst 32, 481–488 (2008). https://doi.org/10.1007/s10916-008-9154-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-008-9154-8

Keywords

Search

Navigation