Automatic Localization and Boundary Detection of Retina in Images Using Basic Image Processing Filters

  • Omar S. Soliman
  • Jan Platoš
  • Aboul Ella Hassanien
  • Václav Snášel
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 179)


This paper proposes an automatic localization and boundary detection of retina images using basic filters to support ophthalmologists for detection and diagnoses eyes harmful diseases such as glaucoma and diabetic retinopathy accurately and diligently. The proposed system comprising three main phases including preprocessing, segmentation and detection phase. The preprocessing phase is used to enhance retinal image and to remove the noise of the retina image. The second phase is the segmentation for main parts of retinal image including optic disc, blood vessels, and fovea to extract their features. Optic disc is segmented using color intensity, the region of interest (ROI) is detected and morphological operations are applied to reduce search complexity. Also, fovea feature is extracted and the blood vessels tree is extracted from retinal image using line detection techniques. The third phase is the detection, in which identification and classifying whether the input image is left or right eye, to support ophthalmologists in identifying which eye is infected by the disease and to check it periodically. Basic image processing filters including average filter, median filter, spatial filter and morphological filter are used in all system phases. Moreover, a simple approach were used to detect left and right retinal fundus images. The proposed system is tested and evaluated using a subset of ophthalmologic images of the publically available DRIVE database.


Diabetic Retinopathy Optic Disc Retinal Image Detection Phase Boundary Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Welfer, D., Scharcanski, J., Kitamura, C.M., Pizzol, M.M.D., Ludwig, L.W.B., Marinho, D.R.: Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach. Comput. Biol. Med. 40, 124–137 (2010)CrossRefGoogle Scholar
  2. 2.
    Siddalingaswamy, P.C.: Gopalakrishna Prabhu, K.: Automatic localization and boundary detection of optic disc using implicit active contours. Int. J. Comput. Appl. 1(7), 1–5 (2010)Google Scholar
  3. 3.
    Winder, R.J., Morrow, P.J., McRitchie, I.N., Bailie, J.R., Hartd, P.M.: Algorithms for digital image processing in diabetic retinopathy. Comput. Med. Imaging Graph. 33, 608–622 (2009)CrossRefGoogle Scholar
  4. 4.
    Sanchez, C.I., Hornero, R., Lyopez, M.I., Aboy, M., Poza, J., Abasolo, D.: A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis. Med. Eng. Phys. 30, 350–357 (2008)CrossRefGoogle Scholar
  5. 5.
    Santhi, D., Karkuzhali, S., Manimegalai, D.: Locating optic disc center and segmenting blood vessels in retinal images for screening system of diabetic retinopathy. 1(1), (2011)Google Scholar
  6. 6.
    Fang, G., Yang, N., Lu H., Li, K.: Automatic segmentation of hard exudates in fundus images based on boosted soft segmentation. In: International Conference on Intelligent Control and Information Processing, Dalian, China, 13–15 Aug 2010Google Scholar
  7. 7.
    Zhang, B., Zhang, L., Zhang, L., Karray, F.: Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Comput. Biol. Med. 40, 438–445 (2010)CrossRefGoogle Scholar
  8. 8.
    Calvo, D., Ortega, M., Penedo, M.G., Rouco, J.: Automatic detection and characterisation of retinal vessel tree bifurcations and crossovers in eye fundus images. J. Comput. Methods Progr. Biomed. 86, 707–718 (2010)Google Scholar
  9. 9.
    Yun, W.L., Acharya, U.R., Venkatesh, Y.V., Chee, C., Min, L.C., Ngd, E.Y.K.: Identification of different stages of diabetic retinopathy using retinal optical images. Inf. Sci. 178, 106–121 (2008)CrossRefGoogle Scholar
  10. 10.
    Garcia, M., Lopezb, M.I., Alvareza, D., Horneroa, R.: Assessment of four neural network based classifiers to automatically detect red lesions in retinal images. Med. Eng. Phys. 32, 1085–1093 (2010)CrossRefGoogle Scholar
  11. 11.
    Gonzales, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Upper Saddle River (2008)Google Scholar
  12. 12.
    Kwan, H.K.: Fuzzy filters for noisy image filtering. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), vol. 4, pp. 161–164 (2003)Google Scholar
  13. 13.

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Omar S. Soliman
    • 1
  • Jan Platoš
    • 2
  • Aboul Ella Hassanien
    • 1
  • Václav Snášel
    • 2
  1. 1.Faculty of Computers and InformationCairo UniversityCairoEgypt
  2. 2.Faculty of Electrical Engendering and Computer ScienceVSB-Technical University of OstravaOstravaCzech Republic

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