Detection of Retinal Vascular Bifurcations by Rotation- and Scale-Invariant COSFIRE Filters

  • George Azzopardi
  • Nicolai Petkov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)


The analysis of the vascular tree in retinal fundus images is important for identifying risks of various cardiovascular diseases. We propose trainable COSFIRE (Combination Of Shifted FIlter REsponses) filters to detect vascular bifurcations. A COSFIRE filter is automatically configured to be selective for a bifurcation that is specified by a user from a training image. The configuration selects given channels of a bank of Gabor filters and determines certain blur and shift parameters. A COSFIRE filter response is computed as the product of the blurred and shifted responses of the selected Gabor filters. The filter responds to bifurcations that are similar to the one used for its configuration. The proposed filters achieve invariance to rotation and scale. With only five COSFIRE filters we achieve a recall of 98.77% at a precision of 95.32% on a data set of 40 binary fundus images (from DRIVE), containing more than 5000 bifurcations.


DRIVE Gabor filters retinal fundus trainable filters vessel bifurcation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ali, C., Hong, S., Turner, J., Tanenbaum, H., Roysam, B.: Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Transactions on Information Technology in Biomedicine 3, 125–138 (1999)CrossRefGoogle Scholar
  2. 2.
    Azzopardi, G., Petkov, N.: Detection of Retinal Vascular Bifurcations by Trainable V4-Like Filters. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W., et al. (eds.) CAIP 2011, Part I. LNCS, vol. 6854, pp. 451–459. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Bhuiyan, A., Nath, B., Chua, J., Ramamohanarao, K.: Automatic detection of vascular bifurcations and crossovers from color retinal fundus images. In: Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (SITIS), pp. 711–718 (2007)Google Scholar
  4. 4.
    Chanwimaluang, T., Guoliang, F.: An efficient blood vessel detection algorithm for retinal images using local entropy thresholding. In: Proceedings of the 2003 IEEE International Symposium on Circuits and Systems (Cat. No.03CH37430) (2003)Google Scholar
  5. 5.
    Chapman, N., Dell’omo, G., Sartini, M., Witt, N., Hughes, A., Thom, S., Pedrinelli, R.: Peripheral vascular disease is associated with abnormal arteriolar diameter relationships at bifurcations in the human retina. Clinical Science, 103Google Scholar
  6. 6.
    Eunhwa, J., Kyungho, H.: Automatic retinal vasculature structure tracing and vascular landmark extraction from human eye image. In: International Conference on Hybrid Information Technology, 7 (2006)Google Scholar
  7. 7.
    Gheorghiu, E., Kingdom, F.: Multiplication in curvature processing. Journal of Vision 9 (2009)Google Scholar
  8. 8.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  9. 9.
    Martinez-Perez, M., Hughes, A., Stanton, A., Thom, S., Chapman, N., Bharath, A., Parker, K.: Retinal vascular tree morphology: A semi-automatic quantification. IEEE Transactions on Biomedical Engineering 49, 912–917 (2002)CrossRefGoogle Scholar
  10. 10.
    Murray, C.D.: The physiological principle of minimum work applied to the angle of branching of arteries  9, 835–841 (1926)Google Scholar
  11. 11.
    Pasupathy, A., Connor, C.E.: Responses to contour features in macaque area v4. Journal of Neurophysiology 82, 2490–2502 (1999)Google Scholar
  12. 12.
    Patton, N., Aslam, T., MacGillivray, T., Deary, I., Dhillon, B., Eikelboom, R., Yogesan, K., Constable, I.: Retinal image analysis: Concepts, applications and potential. Progress in Retinal and Eye Research 25, 99–127 (2006)CrossRefGoogle Scholar
  13. 13.
    Petkov, N.: Biologically motivated computationally intensive approaches to image pattern-recognition. Future Generation Computer Systems 11, 451–465 (1995)CrossRefGoogle Scholar
  14. 14.
    Sherman, T.F.: On connecting large vessels to small - the meaning of murray law. Journal of General Physiology 78, 431–453 (1981)CrossRefGoogle Scholar
  15. 15.
    Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23, 501–509 (2004)CrossRefGoogle Scholar
  16. 16.
    Tsai, C., Stewart, C., Tanenbaum, H., Roysam, B.: Model-based method for improving the accuracy and repeatability of estimating vascular bifurcations and crossovers from retinal fundus images. IEEE Transactions on Information Technology in Biomedicine 8, 122–130 (2004)CrossRefGoogle Scholar
  17. 17.
    Tso, M., Jampol, L.: Path-physiology of hypertensive retinopathy. Opthalmology, 89Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • George Azzopardi
    • 1
  • Nicolai Petkov
    • 1
  1. 1.Johann Bernoulli Institute for Mathematics and Computer ScienceUniversity of GroningenThe Netherlands

Personalised recommendations