Skip to main content
SpringerLink
Log in
Book cover

Proceedings of the International Conference on Data Engineering and Communication Technology pp 191–199Cite as

An Automatic Approach to Segment Retinal Blood Vessels and Its Separation into Arteries/Veins

  • Conference paper
  • First Online:

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 468))

Abstract

The retinal fundus image consists of blood vessels which are further classified as arteries and veins. The measurement of retinal microvasculature changes by classifying arteries and veins using image processing opens window to find biomarkers and gives signs related to diabetic retinopathy, hypertensive retinopathy, hyperglycemia and blood pressure, etc. The purpose of this paper is to find major vessels in retinal image and automatically distinguish them into artery and vein. This paper gives an automated approach for artery–vein classification by analyzing graphical vasculature tree extracted from retinal image. Here, the proposed method distinguish the graphical retinal network by classifying each graphical node as end point, intersection point, and separate point node furthermore labeling each graphical links as artery or vein. Finally, artery–vein classification is performed on the basis of structural as well as intensity-based features. We have tested results of this method on publically available DRIVE database.

This is a preview of subscription content, 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   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
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

Learn about institutional subscriptions

References

  1. N. Patton, T. M. Aslam, T. Mac Gillivray, I. J. Deary, B. Dhillon, R. H. Eikelboom, K. Yoge-sa, and I. J. Constable.: Retinal Image Analysis: Concepts, Applications and Potential. In: Progress in Retinal and Eye Research, vol. 25, pp. 99–127 (2006)

    Google Scholar 

  2. T. T. Nguyen and T. Y. Wong.: Retinal Vascular Changes and Diabetic Retinopathy. In: Current Diabetes Reports, vol. 09, pp. 277–283 (2009)

    Google Scholar 

  3. K. Guan, C. Hudson, T. Wong, M. Kisilevsky, R. K. Nrusimhadevara,W. C. Lam, M. Man-delcorn, R. G. Devenyi, and J. G. Flanagan.: Retinal Hemodynamics in Early Diabetic Macular Edema. In: Diabetes, vol. 55, pp. 813–818 (2006)

    Google Scholar 

  4. S. Neubauer, M. Ludtke, C. Haritoglou, S. Priglinger, and A. Kampik.: Retinal Vessel Analysis Reproducibility in Assessing Cardiovascular Disease. In: Optometry and Vision Science, vol. 85, pp. 247–254 (2008)

    Google Scholar 

  5. G. Liew, P. Mitchell, J. Wang, and T. Wong.: Effect of Axial Length On Retinal Vascular Network Geometry. In: American Journal of Ophtholmology, pp. 648–653 (2005)

    Google Scholar 

  6. S. R. Lesage, T. H. Mosley, T. Y. Wong, M. Szklo, D. Knopman, D. J. Catellier, S. R. Cole, R. Klein, J. Coresh, L. H. Coker, and A. R. Sharrett.: Retinal Microvascular Abnormalities and Cognitive Decline-The ARIC 14-year follow-up study. In: Neurology, vol. 73, pp. 862–868 (2009)

    Google Scholar 

  7. J. Leandro, R. Cesar, and H. Jelinek.: Blood Vessels Segmentation in Retina: Preliminary Assessment of the Mathematical Morphology and of The Wavelet Transform Technique. In: XIV Brazilian Symposium on Computer Graphics and Image Processing, pp. 84–90 (2009)

    Google Scholar 

  8. L. Gang, O. Chutatape, and S. Krishnan.: Detection and Measurement of Retinal Vessels in Fundus Images Using Amplitude Modified Second-Order Gaussian Filter. In: IEEE Transactions on Biomedical Engineering, vol. 49, pp. 168–172 (2002)

    Google Scholar 

  9. Hoover, V. Kouznetsova, and M. Goldbaum.: Locating Blood Vessels in Retinal Images by Piecewise Threshold Probing of a Matched Filter Response. In: IEEE Transactions on Medical Imaging, pp. 203–210 (2000)

    Google Scholar 

  10. T.T. Nguyen, J.J. Wang, and T.Y. Wong.: Retinal Vascular Changes In Prediabetes And Prehypertension: New Findings And Their Research And Clinical Implications. In: Diabetes Care, pp. 2708–2715 (2007)

    Google Scholar 

  11. Ciulla TA, Amador AG, Zinman B.: Diabetic Retinopathy and Diabetic Macular Edema: Pathophysiology, Screening, And Novel Therapies. In: Diabetes Care, pp. 2653–2664 (2003)

    Google Scholar 

  12. Leung H, Wang JJ, Rochtchina E, Tan AG, Wong TY, Klein R, Hubbard LD, Mitchell P.: Relationships Between Age, Blood Pressure, and Retinal Vessel Diameters in an Older Population. In: Investigative Ophthalmology & Visual Science PUBLICATION, vol. 44, pp. 2900–2904 (2004)

    Google Scholar 

  13. Wong TY, Islam FM, Klein R, Klein BE, Cotch MF, Castro C, Sharrett AR, Shahar E.: Retinal Vascular Caliber, Cardiovascular Risk Factors, and Inflammation the Multi-Ethnic Study of Atherosclerosis (MESA). In: Investigative Ophthalmology & Visual Science PUBLICATION, vol. 47, pp. 2341–2350 (2006)

    Google Scholar 

  14. Mitchell P, Cheung N, de Haseth K, Taylor B, Rochtchina E, Islam FMA, Wang JJ, Saw SM, Wong TY.: Blood Pressure and Retinal Arteriolar Narrowing in Children. In: Hypertension, pp. 1156–1162 (2007)

    Google Scholar 

  15. M. E. Martinez-Perez, A. D. Hughes, A. V. Stanton, S. a Thom, N. Chapman, A. a Bharath, and K. H. Parker.: Retinal Vascular Tree Morphology: A Semi-Automatic Quantification. In: IEEE Transactions On Biomedical Engineering, pp. 912–917 (2002)

    Google Scholar 

  16. E. Grisan and A. Ruggeri.: A Divide Et Impera Strategy for Automatic Classification of Retinal Vessels into Arteries and Veins. In: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 890–893 (2003)

    Google Scholar 

  17. Kondermann, D. Kondermann, and M. Yan.: Blood Vessel Classification into Arteries and Veins in Retinal Images. In: Proceedings of SPIE 6512, Medical Imaging: Image Processing (2007)

    Google Scholar 

  18. Yanling Ouyang, Qing Shao.: An Easy Method To Differentiate Retinal Arteries From Veins By Spectral Domain Optical Coherence Tomography: Retrospective, Observational Case Series. In: BMC Ophthalmology, pp. 1471–2415 (2014)

    Google Scholar 

  19. K. Rothaus, X. Jiang, and P. Rhiem.: Separation of The Retinal Vascular Graph in Arteries and Veins Based Upon Structural Knowledge. In: Image & Vision Computing, vol. 27, pp. 864–875 (2009)

    Google Scholar 

  20. Relan, E. Trucco.: Retinal Vessel Classification: Sorting Arteries and Veins. In: IEEE International Conference Proceeding engineering in medicine & Biology Society, pp. 7396–7396 (2013)

    Google Scholar 

  21. S. Sivakami, V.K.U. Ahamed Gani.: SVM & Graph Based Artery/Vein Classification in Retinal Images. In: IJIRCCE, pp. 291–295 (2015)

    Google Scholar 

  22. DRIVE: Digital Retinal Images for Vessel Extraction, http://www.isi.uu.nl/Research/Databases/DRIVE/

Download references

Acknowledgments

The author would like to thank the Signal Processing department for pursuing this work; we have received help and support from all corners; and my family and friends for the encouragement and support. We convey our sincere thanks to authors of DRIVE dataset for making retinal image dataset open source.

Author information

Authors and Affiliations

  1. Late G. N. Sapkal College of Engineering, Nashik, Maharashtra, India

    Medhane Dipak

  2. Sandip Institute of Technology & Research Center, Nashik, Maharashtra, India

    Shukla Aditi

Authors
  1. Medhane Dipak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shukla Aditi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Medhane Dipak .

Editor information

Editors and Affiliations

  1. Department of Computer Science & Engineering, ANITS, Visakhapatnam, India

    Suresh Chandra Satapathy

  2. Dept. of ECE, Shri Ramswaroop Mem. Group of Prof. Clg, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Sabar Institute of Technology, Tajpur, Sabarkantha, Gujarat, India

    Amit Joshi

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Science+Business Media Singapore

About this paper

Cite this paper

Medhane Dipak, Shukla Aditi (2017). An Automatic Approach to Segment Retinal Blood Vessels and Its Separation into Arteries/Veins. In: Satapathy, S., Bhateja, V., Joshi, A. (eds) Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 468. Springer, Singapore. https://doi.org/10.1007/978-981-10-1675-2_21

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-1675-2_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1674-5

  • Online ISBN: 978-981-10-1675-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation