Skip to main content
SpringerLink
Log in

Automated Retinal Blood Vessel Segmentation Using Fuzzy Mathematical Morphology and Morphological Reconstruction

  • Conference paper
  • First Online:
Artificial Intelligence and Signal Processing (AISP 2013)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 427))

Abstract

Assessment of blood vessels in retinal images is an important factor for the many medical disorders. The retinal vessel evaluation can be done by first extracting the retinal images from the background. The changes in the retinal vessels due to the pathologies can be easily identified by segmenting the retinal vessels. In this paper we describe an automatic method for retinal blood vessels segmentation. Segmentation of retinal vessels is done to identify the early diagnosis of the disease like glaucoma, diabetic retinopathy, macular degeneration, hypertensive retinopathy and arteriosclerosis. In this the blood vessel is segmented using fuzzy morphological operation with the disc shaped structuring element and morphological reconstruction. This method is applied on the publicly available DRIVE database and the experimental results obtained by using green-channel images have been presented. This algorithm has been shown to be a very effective method to detect retinal blood vessels. Our proposed algorithm is simple, easy to be implemented, and the best suited for fast processing applications.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

References

  1. Jain, K.: Fundamentals of Digital Image Processing, vol. 3. Prentice-Hall, Englewood Cliffs (1989)

    MATH  Google Scholar 

  2. Pedersen, L., Grunkin, M., Ersbøll, B., Madsen, K., Larsen, M., Christoffersen, N., et al.: Quantitative measurement of changes in retinal vessel diameter in ocular fundus images. Pattern Recogn. Lett. 21, 1215–1223 (2000)

    Article  MATH  Google Scholar 

  3. Sinthanayothin, C., Boyce, J., Williamson, T., Cook, H., Mensah, E., Lal, S., et al.: Automated detection of diabetic retinopathy on digital fundus images. Diabet. Med. 19, 105–112 (2002)

    Article  Google Scholar 

  4. Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imag. 8, 263–269 (1989)

    Article  Google Scholar 

  5. Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imag. 19, 203–210 (2000)

    Article  Google Scholar 

  6. Du, X., Bui, T.D.: Retinal image segmentation based on Mumford-Shah model and Gabor wavelet filter. In: 2010 20th International Conference on, Pattern Recognition (ICPR), pp. 3384–3387 (2010)

    Google Scholar 

  7. Kumari, V.V., Suriyanarayanan, N.: Blood vessel extraction using wiener filter and morphological operation. Int. J. Comput. Sci. Emerg. Technol 1, 7–10 (2010)

    Google Scholar 

  8. Foracchia, M., Grisan, E., Ruggeri, A.: Extraction and quantitative description of vessel features in hypertensive retinopathy fundus images. In: Book Abstracts 2nd International Workshop on Computer Assisted Fundus Image Analysis, p. 6 (2001)

    Google Scholar 

  9. Yang, Y., Huang, S., Rao, N.: An automatic hybrid method for retinal blood vessel extraction. Int. J. Appl. Math. Comput. Sci. 18, 399–407 (2008)

    Article  MATH  Google Scholar 

  10. Patton, N., Aslam, T.M., MacGillivray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., et al.: Retinal image analysis: concepts, applications and potential. Prog. Retinal Eye Res. 25, 99–127 (2006)

    Article  Google Scholar 

  11. Noronha, K., Nayak, J., Bhat, S.: Enhancement of retinal fundus Image to highlight the features for detection of abnormal eyes. In: 2006 IEEE Region 10 Conference TENCON 2006, pp. 1–4 (2006)

    Google Scholar 

  12. Bouchet, A., Brun, M., Ballarin, V.: Morfología Matemática Difusa aplicada a la segmentación de angiografías retinales. Revista Argentina de Bioingeniería 16, 7–10 (2001)

    Google Scholar 

  13. Bouchet, A., Pastore, J.I., Ballarín, V.L.: Segmentation of medical images using fuzzy mathematical morphology. J. Comput. Sci. Technol. 7, 256–262 (2007)

    Google Scholar 

  14. Gasparri, J., Bouchet, A., Abras, G., Ballarin, V., Pastore, J.: Medical image segmentation using the HSI color space and Fuzzy Mathematical Morphology. J. Phys. Conf. Ser. 332(1), 012033 (2011)

    Article  Google Scholar 

  15. Michielsen, K., De Raedt, H., Kawakatsu, T.: Morphological image analysis. In: Landau, D.P., Lewis, S.P., Schüttler, H.-B. (eds.) Computer Simulation Studies in Condensed-Matter Physics XIII, pp. 87–91. Springer, New York (2001)

    Chapter  Google Scholar 

  16. Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Owen, C.G., Rudnicka, A.R., Barman, S.A.: Retinal vessel extraction using first-order derivative of gaussian and morphological processing. In: Bebis, G. (ed.) ISVC 2011, Part I. LNCS, vol. 6938, pp. 410–420. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  17. Espona, L., Carreira, M.J., Ortega, M., Penedo, M.G.: A Snake for retinal vessel segmentation. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 178–185. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  18. Al-Diri, B., Hunter, A., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE Trans. Med. Imag. 28, 1488–1497 (2009)

    Article  Google Scholar 

  19. Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imag. 25, 1200–1213 (2006)

    Article  Google Scholar 

  20. Espona, L., Carreira, M.J., Penedo, M., Ortega, M.: Retinal vessel tree segmentation using a deformable contour model. In: 19th International Conference on Pattern Recognition, 2008, ICPR 2008, pp. 1–4 (2008)

    Google Scholar 

  21. Vlachos, M., Dermatas, E.: Multi-scale retinal vessel segmentation using line tracking. Comput. Med. Imag. Graph. 34, 213–227 (2010)

    Article  Google Scholar 

  22. Dey, N., Roy, A.B., Pal, M., Das, A.: FCM based blood vessel segmentation method for retinal images. arXiv preprint: arXiv:1209.1181 (2012)

    Google Scholar 

  23. Dai, B., Bu, W., Wu, X., Teng, Y.: Retinal vessel segmentation via iterative geodesic time transform. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 561–564 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical, Computer and Biomedical Engineering Qazvin Branch, Islamic Azad University, Qazvin, Iran

    Razieh Akhavan

  2. Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran

    Karim Faez

Authors
  1. Razieh Akhavan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Karim Faez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Razieh Akhavan .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Ali Movaghar

  2. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Mansour Jamzad

  3. Sharif University of Technology, Tehran, Iran

    Hossein Asadi

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Akhavan, R., Faez, K. (2014). Automated Retinal Blood Vessel Segmentation Using Fuzzy Mathematical Morphology and Morphological Reconstruction. In: Movaghar, A., Jamzad, M., Asadi, H. (eds) Artificial Intelligence and Signal Processing. AISP 2013. Communications in Computer and Information Science, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-319-10849-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10849-0_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10848-3

  • Online ISBN: 978-3-319-10849-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation