Abstract
Diabetic retinopathy is a damage of the retina and it is one of the serious consequences of the diabetes. Early detection of diabetic retinopathy is extremely important in order to prevent premature visual loss and blindness. This paper presents a novel automatic detection of diabetic retinopathy and maculopathy in eye fundus images using fuzzy image processing. The detection of maculopathy is essential as it will eventually cause loss of vision if the affected macula is not timely treated. The developed system consists of image acquisition, image preprocessing with a combination of fuzzy techniques, feature extraction, and image classification by using several machine learning techniques. The fuzzy-based image processing decision support system will assist in the diabetic retinopathy screening and reduce the burden borne by the screening team.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Taylor, R., Batey, D.: Handbook of retinal screening in diabetes: diagnosis and management. John Wiley & Sons Ltd., England (2012)
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.: Proposed International Clinical Diabetic Retinopathy and Diabetic Macula Edema Disease Severity Scales. American Academy of Ophthalmology 110(9), 1677–1682 (2003)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Chowriappa, P., Dua, S., Rajendra, A.U., Muthu, R.K.M.: Ensemble selection for feature-based classification of diabetic maculopathy images. Computers in Biology and Medicine 43(12), 2156–2162 (2013)
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)
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)
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)
Toh, K.K.V., Mat Isa, N.A.: Noise adaptive Fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Processing Letters 17(3), 281–284 (2010)
Kwan, H.K.: Fuzzy filters for noisy image filtering. In: IEEE International Symposium on Circuits and Systems 2003 (ISCAS 2003), vol. 4, pp. 161–164. IEEE, New York (2003)
Duin, R.P.W., Juszczak, P., Paclik, P., Pekalska, E., de Ridder, D., Tax, D.M.J., Verzakov, S.: PRTools4.1, A Matlab Toolbox for Pattern Recognition, Delft University of Technology (2007)
Holzinger, A.: Human-Computer Interaction and Knowledge Discovery (HCI-KDD): What Is the Benefit of Bringing Those Two Fields to Work Together? In: Cuzzocrea, A., Kittl, C., Simos, D.E., Weippl, E., Xu, L. (eds.) CD-ARES 2013. LNCS, vol. 8127, pp. 319–328. Springer, Heidelberg (2013)
Holzinger, A., Malle, B., Giuliani, N.: On Graph Extraction from Image Data. In: Slezak, D., Peters, J.F., Tan, A.-H., Schwabe, L. (eds.) Lecture Notes in Artificial Intelligence, LNAI 8609, pp. 552–563. Springer, Heidelberg, Berlin (2014)
Holzinger, A., Blanchard, D., Bloice, M., Holzinger, K., Palade, V., Rabadan, R.: Darwin, Lamarck, or Baldwin: Applying Evolutionary Algorithms to Machine Learning Techniques. In: The 2014 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2014), pp. 449–453. IEEE (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Rahim, S.S., Palade, V., Jayne, C., Holzinger, A., Shuttleworth, J. (2015). Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds) Brain Informatics and Health. BIH 2015. Lecture Notes in Computer Science(), vol 9250. Springer, Cham. https://doi.org/10.1007/978-3-319-23344-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-23344-4_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23343-7
Online ISBN: 978-3-319-23344-4
eBook Packages: Computer ScienceComputer Science (R0)