Classification of Diabetic Maculopathy from Retinal Images

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Diabetes mellitus is a significant cause for   visible loss and vision deficit. All patients with type 1  diabetes and greater than 60% of type 2 diabetes suffer a few degrees of retinopathy, due to diabetes for a long time. The damage of the normal vision, contingent upon the significant of damage of the macula, is due to diabetes retinopathy, which extends to Maculopathy. The main objective of  this work is to design  a  method and develop software to identify the seriousness of  diabetic maculopathy, using image processing techniques on retinal images. The proposed framework classifies different types of maculopathy as Normal or  clinically important and  non-clinically significant maculopathy from  fundus images. The features had been separated from the original fundus image with morphological operations and strengthened with two classifiers, the Artificial Neural method (ANN) and probabilistic neural methods (PNN). The proposed method  established that ANN has the best characterization performance efficiency of 96.67%  compared  to PNN.




  • Keywords

    Image processing, classification, Diabetic maculopathy, Feature extraction, macular Segmentation.

  • References

      [1] Taylor, R., Batey, D.: Handbook of retinal screening in diabetes: diagnosis and management. John Wiley & Sons Ltd., England (2012)

      [2] Wilkinson, C.P., Ferris, F.L., Klein, R.E., Lee, P.P., Agardh, C.D., Davis, M., Dills, D., Kampik, A., Pararajasegaram, R., Verdaguer, J.T.: International Clinical Diabetic Retinopathy and Diabetic Macula Edema Disease Severity Scales. American Academy of Ophthalmology 110(9), 1677–1682 (2003)

      [3] Early Treatment Diabetic Retinopathy Study Research Group.: Grading diabetic retinopathy from stereoscopic color fundus photographs- an extension of the modified Airlie House classification. ETDRS report number 10. Ophthalmology 98(5 suppl.), 823–833 (1991)

      [4] [Jayne, C., Rahim, S.S., Palade, V., Shuttleworth, J.: Automatic Screening and Classification of Diabetic Retinopathy Fundus Images. In: Mladenov, V., Jayne, C., Iliadis, L. (eds.) EANN 2014. CCIS, vol. 459, pp. 113–122. Springer, Heidelberg (2014)

      [5] Rahim, S.S., Palade, V., Shuttleworth, J., Jayne, C., Raja Omar, R.N.: Automatic detection of microaneurysms for diabetic retinopathy screening using fuzzy image processing. In: Iliadis, L. et al. (eds.) Engineering Applications of Neural Networks. CCIS, vol. 517. Springer, Heidelberg(2015) 388.

      [6] Mookiah, M.R.K., Acharya, U.R., Martis, R.J., Chua, C.K., Lim, C.M., Ng, E.Y.K., Laude, A.: Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowledge-Based Systems 39, 9–22 (2013)

      [7] Priya, R., Aruna, P.: Review of automated diagnosis of diabetic retinopathy using the support vector machine. International Journal of Applied Engineering Research 1(4), 844–863 (2011)

      [8] Vimala, A.G.S.G., Kajamohideen, S.: Detection of diabetic maculopathy in human retinal images using morphological operations. Online J. Biol. Sci. 14, 175–180 (2014)

      [9] Tariq, A., Akram, M.U., Shaukat, A., Khan, S.A.: Automated detection and grading of diabetic maculopathy in digital retinal images. J. Digit Imaging 26(4), 803–812 (2013)

      [10] Siddalingaswamy, P.C., Prabhu, K.G.: Automatic grading of diabetic maculopathy severity levels. In: 2010 International Conference on Systems in Medicine and Biology, pp. 331–334. IEEE, New York (2010)

      [11] Punnolil, A.: A novel approach for diagnosis and severity grading of diabetic maculopathy. In: 2013 International Conference on Advances in Computing, Communications and Informatics, pp. 1230–1235. IEEE, New York (2013)

      [12] Hunter, A., Lowell, J.A., Steel, D., Ryder, B., Basu, A.: Automated diagnosis of referable maculopathy in diabetic retinopathy screening. In: Annual international of the IEEE Engineering in Medicine and Bilogy Society, EMBS, pp. 3375–3378. IEEE, New York (2011)

      [13] Chowriappa, P., Dua, S., Rajendra, A.U., Muthu, R.K.M.: Ensemble selection for featurebased classification of diabetic maculopathy images. Computers in Biology and Medicine 43(12), 2156–2162 (2013)

      [14] Sheet, D., Garud, H., Suveer, A., Mahadevappa, M., Chatterjee, J.: Brightness preserving dynamic Fuzzy Histogram Equalization. IEEE Transactions on Consumer Electronics 56(4), 2475–2480 (2010)

      [15] Garud, H., Sheet, D., Suveer, A., Karri, P.K., Ray, A.K., Mahadevappa, M., Chatterjee, J.: Brightness preserving contrast enhancement in digital pathology. In: 2011 International Conference on Image Information Processing (ICIIP 2011), pp. 1–5. IEEE, New York (2011)

      [16] Patil, J., Chaudhari, A.L.: Development of digital image processing using Fuzzy Gaussian filter tool for diagnosis of eye infection. International Journal of Computer Applications 51(19), 10–12 (2012)

      [17] Toh, K.K.V., Mat Isa, N.A.: Noise adaptive Fuzzy switching median filter for salt-andpepper noise reduction. IEEE Signal Processing Letters 17(3), 281–284 (2010) .




Article ID: 28708
DOI: 10.14419/ijet.v7i4.22.28708

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.