Optic Disc Localization in Retinal Images

  • Florin Rotaru
  • Silviu Ioan Bejinariu
  • Cristina Diana Niţă
  • Mihaela Costin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 195)

Abstract

The paper proposes an optic disc localisation method in color retinal images. It is a first step of a retinal image analysis project which will be completed later with other tasks as fovea detection and measurement of retinal vessels. The final goal is to detect in early stages signs of ophthalmic pathologies as diabetic retinopathy or glaucoma, by successive analysis of ophthalmoscopy images. The proposed method first detects in the green component of RGB image the optic disc area and then on the segmented area extracts the optic disc edges and obtains a circular optic disc boundary approximation by a Hough transform.

Keywords

optic disc retinal images vessel segmentation Hough transforms 

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References

  1. 1.
    Aquino, A., Gegundez-Arias, M.E., Marin, D.: Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques. IEEE Transactions on Medical Imaging 29(11), 1860–(1869)CrossRefGoogle Scholar
  2. 2.
    Manikis, G.C., Sakkalis, V., Zabulis, X., Karamaounas, P., Triantafyllow, A., Douma, S., Zamboulis, C., Marias, K.: An Image Analysis Framework for the Early Assesement of Hypertensive Retinopathy Signs. In: Proceedings of the 3rd IEEE International Conference on E-Health and Bioengineering - EHB 2011, Iaşi, Romania, November 24-26, pp. 413–418 (2011)Google Scholar
  3. 3.
    Li, H., Chutatape, O.: Automated Feature Extraction in Color Retinal Images by a Model Based Approach. IEEE Transactions on Biomedical Engineering 51(2), 246–254 (2004)CrossRefGoogle Scholar
  4. 4.
    Hoover, A., Goldbaum, M.: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imag. 22(8), 951–958 (2003)CrossRefGoogle Scholar
  5. 5.
    Foracchia, M., Grisan, E., Ruggeri, A.: Detection of optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Trans. Med. Imag. 23(10), 1189–1195 (2004)CrossRefGoogle Scholar
  6. 6.
    Heneghan, C., Flynn, J., O’Keefe, M., Cahill, M.: Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis. Med. Image Anal. 6, 407–429 (2002)CrossRefGoogle Scholar
  7. 7.
    Lindeberg, T.: Detecting salient blob-like image structures and their scales with a scale-space primal sketch: A method for focus-of attention. International Journal of Computer Vision 11, 283–318 (1993)CrossRefGoogle Scholar
  8. 8.
    Guo, Y.: Computer-Aided Detection of Breast Cancer Using Ultrasound Images. PhD Thesis, Utah State University (2010)Google Scholar
  9. 9.
    Otsu, N.: A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62–66 (1979)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Florin Rotaru
    • 1
  • Silviu Ioan Bejinariu
    • 1
  • Cristina Diana Niţă
    • 1
  • Mihaela Costin
    • 1
  1. 1.Institute of Computer ScienceRomanian Academy, Iasi BranchIasiRomania

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