Skip to main content

A Snake for Retinal Vessel Segmentation

  • Conference paper
Pattern Recognition and Image Analysis (IbPRIA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4478))

Included in the following conference series:

Abstract

This paper presents an innovative methodology to detect the vessel tree in retinal angiographies. The automatic analysis of retinal vessel tree facilitates the computation of the arteriovenous index, which is essential for the diagnosis of a wide range of eye diseases. We have developed a system inspired in the classical snake but incorporating domain specific knowledge, such as blood vessels topological properties. It profites mainly from the automatic localization of the optic disc and from the extraction and enhancement of the vascular tree centerlines. Encouraging results in the detection of arteriovenous structures are efficiently achieved, as shown by the systems performance evaluation on the publicy available DRIVE database.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Niemeijer, M., van Ginneken, B., Staal, J., Suttorp-Schulten, M.S.A., Abràmoff, M.D.: Automatic Detection of Red Lesions in Digital Color Fundus Photographs. IEEE Transactions on Medical Imaging 24(5), 584–592 (2005)

    Article  Google Scholar 

  2. Aurell, E., et al.: A note of signs in the fundus oculi and arterial hypertension conventional assessment and significance. Bull. World Health Organ. 34, 955–960 (1967)

    Google Scholar 

  3. Mendoça, A.M., Campilho, A.: Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines and Morphological Reconstruction. IEEE Transactions on Medical Imaging 25(9), 1200–1213 (2006)

    Article  Google Scholar 

  4. Soares, J.V.B., Leandro, J.J.G., Cesar Jr., R.M.C., Jelinek, H.F., Cree, M.J.: Retinal Vessel Segmentation Using the 2-D Gabor Wavelet and Supervised Classification. IEEE Transactions on Medical Imaging 25(9), 1214–1222 (2006)

    Article  Google Scholar 

  5. Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abràmoff, M.D.: Comparative Study of Retinal Vessel Segmentation Methods on a new Publicy Avaliable Database. In: Proceedings of the SPIE. Medical Imaging 2004: Image Processing, vol. 5370, pp. 648–656 (2004)

    Google Scholar 

  6. Toledo, R., Orriols, X., Binefa, X., Redeva, P., Vitri, J., Villanueva, J.J.: Tracking elongated structures using statistical snakes. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, vol. 1(1), pp. 157–162 (2000)

    Google Scholar 

  7. Caderno, I.G., Penedo, M.G., Mariño, C., Carreira, M.J., Gomez-Ulla, F., González, F.: Automatic Extraction of the Retina AV Index. In: Campilho, A.C., Kamel, M. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 132–140. Springer, Heidelberg (2004)

    Google Scholar 

  8. Ortega, M., Mariño, C., Penedo, M.G., Blanco, M., González, F.: Personal Authentication based on Feature Extraction and Optic Nerve Location in Digital Retinal Images. Wseas Transactions on Computers 5(6), 1169–1176 (2006)

    Google Scholar 

  9. Kass, M., Witkin, A., Terzopoulos, D.: Active Contour Models. International Journal of Computer Vision 1(2), 321–331 (1998)

    Google Scholar 

  10. Canny, J.A.: Computational Approach to Edge-Detection. IEEE Transactions on Pattern Analysis and Machine Inteligence 8(6), 679–689 (1986)

    Article  Google Scholar 

  11. Blanco, M., Penedo, M.G., Barreira, N., Penas, M., Carreira, M.J.: Localization and Extraction of the Optic Disc Using the Fuzzy Circular Hough Transform. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 712–721. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  12. Staal, J.J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23, 501–509 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Joan Martí José Miguel Benedí Ana Maria Mendonça Joan Serrat

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Espona, L., Carreira, M.J., Ortega, M., Penedo, M.G. (2007). A Snake for Retinal Vessel Segmentation. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72849-8_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-72849-8_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72848-1

  • Online ISBN: 978-3-540-72849-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics