Automated Arteriole and Venule Recognition in Retinal Images using Ensemble Classification

M. M. Fraz, A. R. Rudincka, C. G. Owen, D. P. Strachan, S. A. Barman

Abstract

The shape and size of retinal vessels have been prospectively associated with cardiovascular outcomes in adult life, and with cardiovascular precursors in early life, suggesting life course patterning of vascular development. However, the shape and size of arterioles and venules may show similar or opposing associations with disease precursors / outcomes. Hence accurate detection of vessel type is important when considering cardio-metabolic influences on vascular health. This paper presents an automated method of identifying arterioles and venules, based on colour features using the ensemble classifier of boot strapped decision trees. The classifier utilizes pixel based features, vessel profile based features and vessel segment based features from both RGB and HIS colour spaces. To the best of our knowledge, the decision trees based ensemble classifier has been used for the first time for arteriole/venule classification. The classification is performed across the entire image, including the optic disc. The methodology is evaluated on 3149 vessel segments from 40 colour fundus images acquired from an adult population based study in the UK (EPIC Norfolk), resulting in 83% detection rate. This methodology can be further developed into an automated system for measurement of arterio-venous ratio and quantification of arterio-venous nicking in retinal images, which may be of use in identifying those at high risk of cardiovascular events, in need of early intervention.

References

  1. Abràmoff, M. D., M. K. Garvin, et al. (2010). "Retinal Imaging and Image Analysis." Biomedical Engineering, IEEE Reviews in 3: 169-208.
  2. Dashtbozorg, B., A. M. Mendonca, et al. (2013). "An Automatic Graph-based Approach for Artery/Vein Classification in Retinal Images." Image Processing, IEEE Transactions on PP(99): 1-1.
  3. EPIC-Norfolk. (2013). "European Prospective Investigation of Cancer (EPIC)." Retrieved September, 2013, from http://www.srl.cam.ac.uk/epic/.
  4. Fraz, M. M., S. A. Barman, et al. (2012). "An approach to localize the retinal blood vessels using bit planes and centerline detection." Computer methods and programs in biomedicine 108(2): 600-616.
  5. Fraz, M. M., P. Remagnino, et al. (2013). "Quantification of blood vessel calibre in retinal images of multiethnic school children using a model based approach." Computerized Medical Imaging and Graphics 37(1): 60-72.
  6. Fraz, M. M., P. Remagnino, et al. (2012). "Blood vessel segmentation methodologies in retinal images - A survey." Computer methods and programs in biomedicine 108(1): 407-433.
  7. Fraz, M. M., P. Remagnino, et al. (2012). "An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation." Biomedical Engineering, IEEE Transactions on 59(9): 2538-2548.
  8. Grisan, E. and A. Ruggeri (2003). A divide et impera strategy for automatic classification of retinal vessels into arteries and veins. Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE.
  9. Huang, Y., J. Zhang, et al. (2012). "An automated computational framework for retinal vascular network labeling and branching order analysis." Microvascular Research 84(2): 169-177.
  10. Jack J. Kanski and Brad Bowling (2011). Clinical Ophthalmology: A Systematic Approach. London, Elsevier Health Sciences (UK).
  11. Nguyen, U., A. Bhuiyan, et al. (2013). "An Automated Method for Retinal Arteriovenous Nicking Quantification from Colour Fundus Images." Biomedical Engineering, IEEE Transactions on PP(99): 1-1.
  12. Nguyen, U. T. V., A. Bhuiyan, et al. (2012). "An effective retinal blood vessel segmentation method using multiscale line detection." Pattern Recognition(0).
  13. Niemeijer, M., X. Xiayu, et al. (2011). "Automated Measurement of the Arteriolar-to-Venular Width Ratio in Digital Color Fundus Photographs." Medical Imaging, IEEE Transactions on 30(11): 1941-1950.
  14. Owen, C. G., A. R. Rudnicka, et al. (2011). "Retinal Arteriolar Tortuosity and Cardiovascular Risk Factors in a Multi-Ethnic Population Study of 10-Year-Old Children; the Child Heart and Health Study in England (CHASE)." Arteriosclerosis, Thrombosis, and Vascular Biology 31(8): 1933-1938.
  15. Polikar, R. (2006). "Ensemble Based Systems in Decision Making." IEEE Circuits and Systems Magazine 6(3): 21-45.
  16. Relan, D., T. MacGillivray, et al. (2013). Retinal vessel classification: sorting arteries and veins. 35th Annual International Conference of the IEEE EMBS Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, IEEE.
  17. Ricci, E. and R. Perfetti (2007). "Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification." Medical Imaging, IEEE Transactions on 26(10): 1357-1365.
  18. Rothaus, K., X. Jiang, et al. (2009). "Separation of the retinal vascular graph in arteries and veins based upon structural knowledge." Image and Vision Computing 27(7): 864-875.
  19. Saez, M., S. González-Vázquez, et al. (2012). "Development of an automated system to classify retinal vessels into arteries and veins." Computer methods and programs in biomedicine 108(1): 367- 376.
  20. Vázquez, S. G., B. Cancela, et al. (2013). "Improving retinal artery and vein classification by means of a minimal path approach." Machine Vision and Applications 24(5): 919-930.
  21. Wong, T. Y., R. Klein, et al. (2001). "Retinal microvascular abnormalities and incident stroke: the Atherosclerosis Risk in Communities Study." The Lancet 358(9288): 1134-1140.
Download


Paper Citation


in Harvard Style

M. Fraz M., R. Rudincka A., G. Owen C., P. Strachan D. and A. Barman S. (2014). Automated Arteriole and Venule Recognition in Retinal Images using Ensemble Classification . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-009-3, pages 194-202. DOI: 10.5220/0004733701940202


in Bibtex Style

@conference{visapp14,
author={M. M. Fraz and A. R. Rudincka and C. G. Owen and D. P. Strachan and S. A. Barman},
title={Automated Arteriole and Venule Recognition in Retinal Images using Ensemble Classification},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={194-202},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004733701940202},
isbn={978-989-758-009-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2014)
TI - Automated Arteriole and Venule Recognition in Retinal Images using Ensemble Classification
SN - 978-989-758-009-3
AU - M. Fraz M.
AU - R. Rudincka A.
AU - G. Owen C.
AU - P. Strachan D.
AU - A. Barman S.
PY - 2014
SP - 194
EP - 202
DO - 10.5220/0004733701940202