Abstract
Diabetes is a condition of increase in the blood sugar level higher than the normal range. Prolonged diabetes damages the small blood vessels in the retina resulting in diabetic retinopathy (DR). DR progresses with time without any noticeable symptoms until the damage has occurred. Hence, it is very beneficial to have the regular cost effective eye screening for the diabetes subjects. This paper documents a system that can be used for automatic mass screenings of diabetic retinopathy. Four classes are identified: normal retina, non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and macular edema (ME). We used 238 retinal fundus images in our analysis. Five different texture features such as homogeneity, correlation, short run emphasis, long run emphasis, and run percentage were extracted from the digital fundus images. These features were fed into a support vector machine classifier (SVM) for automatic classification. SVM classifier of different kernel functions (linear, radial basis function, polynomial of order 1, 2, and 3) was studied. Receiver operation characteristics (ROC) curves were plotted to select the best classifier. Our proposed system is able to identify the unknown class with an accuracy of 85.2%, and sensitivity, specificity, and area under curve (AUC) of 98.9%, 89.5%, and 0.972 respectively using SVM classifier with polynomial kernel of order 3. We have also proposed a new integrated DR index (IDRI) using different features, which is able to identify the different classes with 100% accuracy.
Similar content being viewed by others
References
Samuel, C. L., Elisa, T. L., Yiming, W., Ronald, K., Ronald, M. K., and Ann, W., Computer classification of a non-proliferative diabetic retinopathy. Arch. Ophthalmol. 123:759–764, 2005.
Fong, D. S., Aiello, L., Gardner, T. W., King, G. L., Blankenship, G., Cavallerano, J. D., Ferris, F. L., and Klein, R., Diabetic retinopathy. Diab. Care 26(1):226–229, 2003.
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, 2004.
Albregtsen, F., Statistical texture measures computed from gray level run length matrices, 1995.
Gardner, G., Keating, D., Williamson, T., and Elliott, A., Automatic detection of diabetic retinopathy using an artificial neural network: A screening tool. Br. J. Ophthalmol. 80:940–944, 1996.
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(3):445–452, 2004.
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.
Wang, H., Hsu, W., Goh, K., and Lee, M., An effective approach to detect lesions in colour retinal images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 181–187, 2000.
Hayashi, J., Kunieda, T., Cole, J., Soga, R., Hatanaka, Y., Lu, M., Hara, T., and Fujita, H., A development of computer-aided diagnosis system using fundus images, Proceeding of the 7th International Conference on Virtual Systems and MultiMedia (VSMM 2001), pp. 429–438, 2001.
Tan, J. H., Ng, E. Y. K., and Acharya, U. R., Study of normal ocular thermogram using textural parameters. Infrared Phys. Technol., 2009.
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., 2008.
Weszka, J. S., and Rosenfield, A., An application of texture analysis to material inspection. Pattern Recognit. 8:195–200, 1976.
Guan, K., Hudson, C., Wong, T., Kisilevsky, M., Nrusimhadevara, R. K., Lam, W. C., Mandelcorn, M., Devenyi, R. G., and Flanagan, J. G., Diabetes 55:813–818, 2006.
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.
Galloway, M. M., Texture classification using gray level run length. Comput. Graph. Image Process. 4:172–179, 1975.
Niemeijer, M., van Ginneken, B., Staal, J., Suttorp-Schulten, M., and Abramoff, M., Automatic detection of red lesions in digital color fundus photographs. IEEE Trans. Med. Imaging 24(5):584–592, 2005.
Tuceryan, M., and Jain, A. K., Texture analysis. In: Chen, C. H., Pau, L. F., and Wang, P. S. P. (Eds.), Handbook of Pattern Recognition & Computer Vision, 1993.
Kahai, P., Namuduri, K. R., and Thompson, H., A decision support framework for automated screening of diabetic retinopathy. Int. J. Biomed. Imaging 1–8, 2006.
Bremananth, R., Nithya, B., and Saipriya, R., Wood species recognition system using GLCM and correlation, Proc. IEEE Computer Society, Int. Conf. ARTCOM 27–28:615–619, 2009.
Gonzalez, R. C., and Woods, R. E., Digital image processing, 2nd edition. Prentice Hall, New Jersey, 2001.
Frank, R. N., Diabetic retinopathy. Prog. Retin. Eye Res. 14(2):361–392, 1995.
Bailey, R. R., Moments in Image Processing, 2002.
Screening for Diabetic Retinopathy in Europe 15 years after the St. Vincent Declaration, 2005. Available from: http://reseau-ophdiat.aphp.fr/Document/Doc/confliverpool.pdf.
Silakari, S., Motwani, M., and Maheshwari, M., Color image clustering using block truncation algorithm. IJCSI Int. J. Comput. Sci. Issues 4:31–35, 2009.
Standards of medical care for patients with diabetes mellitus. Diabetes Care 25:33S–49, 2002.
Acharya, U. R., Chua, K. C., Ng, E. Y. K., Wei, W., and Chee, C., Application of higher order spectra for the identification of diabetic retinopathy stages. J. Med. Syst. 32(6):481–488, 2008.
Acharya, U. R., Lim, C. M., Ng, E. Y. K., Chee, C., and Tamura, T., Computer based detection of diabetic retinopathy stages using digital fundus images. J. Eng. Med. 223(H5):545–553, 2009.
Press, W. H., Flannery, B. P., Teukolsky, S. A., and Vetterling, W. T., Numerical recipes in C: the art of scientific computing. Cambridge University Press, New York, 1990.
Xiaohui, Z., and Chutatape, O., Detection and classification of bright lesions in colour fundus Images. Int. Conf. Image Process. 1:139–142, 2004.
Acknowledgement
Authors thank MESSIDOR database (http://messidor.crihan.fr) for providing the fundus images for this work.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Acharya, U.R., Ng, E.Y.K., Tan, JH. et al. An Integrated Index for the Identification of Diabetic Retinopathy Stages Using Texture Parameters. J Med Syst 36, 2011–2020 (2012). https://doi.org/10.1007/s10916-011-9663-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10916-011-9663-8