Advertisement

Extraction of Blood Vessels in Ophthalmic Color Images of Human Retinas

  • Edgardo Felipe-Riveron
  • Noel Garcia-Guimeras
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)

Abstract

This paper presents a strategy for the extraction of blood vessels from ophthalmoscopic color images of the fundus of human retinas. To extract the vascular network, morphology operators were used, primarily maximum of openings and sum of valleys, and secondly a reconstruction by dilation from two images obtained using threshold by hysteresis. To extract the skeleton of the resulting vascular network, morphological thinning and pruning algorithms were used. Results obtained represent a starting point for future work related to the detection of anomalies in the vascular network and techniques for personal authentication.

Keywords

Blood vessels segmentation Fundus analysis Morphology 

References

  1. 1.
    Chaudhury, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Transactions on medical imaging 8(3) (1989)Google Scholar
  2. 2.
    Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of matched filter response. IEEE Transactions on Medical Imaging 19(3) (2000)Google Scholar
  3. 3.
    Chutatape, O., Zheng, L., Krishnan, S.M.: Retinal blood vessel detection and tracking by matched gaussian and Kalman filters. In: Proceedings in the 20th Annual International Conference of the IEEE, Engineering in Medicine and Biology Society, vol. 20(6) (1998)Google Scholar
  4. 4.
    Gardner, G.G., Keating, D., Williamson, T.H., Elliot, A.T.: Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. British Journal of Ophthalmology (1996)Google Scholar
  5. 5.
    Undrill, P.: Towards the automatic interpretation of retinal images. British Journal of Ophthalmology 80, 973 (1996)CrossRefGoogle Scholar
  6. 6.
    Zana, F., Klein, J.C.: A multimodal algorithm of eye fundus images using vessel detection and Hough transform. IEEE Transactions on Medical Imaging 18(5) (1999)Google Scholar
  7. 7.
    Zhoue, I., Rzeszotarski, M., Singerman, L., Cokreff, J.: The detection and quantification of retinopathy using digital angiograms. IEEE Transactions on Medical Imaging 13(4) (1994)Google Scholar
  8. 8.
    Zana, F., Klein, J.C.: Robust segmentation of vessels from retinal angiography. In: Proceedings of the International Conference on Digital Signal Processing, Santorini, Greece (1997)Google Scholar
  9. 9.
    Zana, F., Klein, J.C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Transactions on Image Processing 10(7) (2001)Google Scholar
  10. 10.
    Flynn, J.: Automated vessel extraction in digital ophthalmic imagesGoogle Scholar
  11. 11.
    Gang, L., Chutatape, O., Krishnan, S.M.: Detection and measurement of retinal vessels in fundus images using amplitude modified second-order gaussian filter. IEEE Transactions on Biomedical Engineering 49(2) (2002)Google Scholar
  12. 12.
    Gonzalez, R., Woods, R.E.: Digital Image Processing. Addison-Wesley, Imington (1996)Google Scholar
  13. 13.
    Soille, P.: Morphological Image Analysis. Principles and Applications. Springer, Berlin (1999)zbMATHGoogle Scholar
  14. 14.
    Osareh, A., Mirmehdi, M., Thomas, B., Markham, R.: Automatic Recognition of Exudative Maculopathy using Fuzzy C-Means Clustering and Neural Networks. Medical Image Understanding and Analysis, 49–52 (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Edgardo Felipe-Riveron
    • 1
  • Noel Garcia-Guimeras
    • 2
  1. 1.Centre for Computing ResearchNational Polytechnic InstituteMexico
  2. 2.Latin American School of MedicineHavanaCuba

Personalised recommendations