Classification of Artery and Vein in Retinal Fundus Images Based on the Context-Dependent Features

  • Yang Yan
  • Dunwei Wen
  • M. Ali Akber Dewan
  • Wen-Bo Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10286)


In this paper, we present an automatic method based on context-dependent characteristics for the detection and classification of arterial vessels and venous vessels in retinal fundus images. It provides a non-invasive opportunity and effective foundation for the diagnosis of several medical pathologies. In the proposed method, a combination of shifted filter responses is used, which can selectively respond to vessels. It achieves orientation selectivity by computing the weighted geometric mean of the output of a pool of Difference-of-Gaussian filters, whose supports are aligned in a collinear manner. We then configure two combinations of shifted filters, namely symmetric and asymmetric, that are selective for bars and bar-endings, respectively. We achieve vessel detection by summing up the responses of the two filters. Then we extract the morphology and topological characteristics based on the vessel segmentation, and specifically present context-dependent features of blood vessels, including the shape, structure, relative position, context information and other important features. Based on these features, we use JointBoost classifier to construct potential function for conditional random fields (CRFs) model, and train the labeled samples to classify arteriovenous blood vessels in retinal images. The training and testing data sets were prepared according to the results based on DRIVE dataset provided by Estrada et al. The experimental results show that the accuracy of the proposed method for vein and artery detection is 91.1% and 94.5%, respectively, which is superior to that of the state-of-the-art methods. It can be used as a clinical reference for computer-assisted quantitative analysis of fundus images.


Retinal image analysis Vessel segmentation Classification of artery and vein Combination of shifted filter responses Context-dependent features Conditional random fields 



This research is funded by Natural Science Foundation of Changchun Normal University in 2015 (contract number: CCNU Natural Science Co-words [2015] No. 005) and Scientific Research Planning Project of the Education Department of Jilin Province in 2016 (contract number: Ji Edu & Sci Co-words [2016] No. 001). It is also supported by China scholarship Council under the State Scholarship Fund.


  1. 1.
    Liew, G., Sim, D.A., Keane, P.A., et al.: Diabetic macular ischaemia is associated with narrower retinal arterioles in patients with type 2 diabetes. Acta Ophthalmol. 93(1), e45–e51 (2015)CrossRefGoogle Scholar
  2. 2.
    Wong, T.Y., Klein, R., Sharrett, A.R., et al.: Retinal arteriolar diameter and risk for hypertension. Ann. Intern. Med. 140(4), 248–255 (2004)CrossRefGoogle Scholar
  3. 3.
    Nguyen, T.T., Wang, J.J., Wong, T.Y.: Retinal vascular changes in pre-diabetes and prehypertension. Diabetes Care 30(10), 2708–2715 (2007)CrossRefGoogle Scholar
  4. 4.
    Macgillivray, T.J., Patton, N., Doubal, F.N., et al.: Fractal analysis of the retinal vascular network in fundus images. In: The IEEE 29th Annual International Conference of Engineering in Medicine and Biology Society, pp. 6455–6458 (2007)Google Scholar
  5. 5.
    Grisan, E., Foracchia, M., Ruggeri, A.: A novel method for the automatic grading of retinal vessel tortuosity. IEEE Trans. Med. Imaging 27(3), 310–319 (2008)CrossRefGoogle Scholar
  6. 6.
    Grisan, E., Ruggeri, A.: A divide et impera strategy for automatic classification of retinal vessels into arteries and veins. In: Proceedings of the IEEE 25th Annual International Conference of Engineering in Medicine and Biology Society, vol. 1, pp. 890–893 (2003)Google Scholar
  7. 7.
    Jelinek, H.F., Depardieu, C., Lucas, C., et al.: Towards vessel characterisation in the vicinity of the optic disc in digital retinal images. In: Image and Vision Computing Conference, pp. 2–7 (2005)Google Scholar
  8. 8.
    Kondermann, C., Kondermann, D., Yan, M.: Blood vessel classification into arteries and veins in retinal images. In: Medical Imaging. International Society for Optics and Photonics, pp. 651247–651247-9 (2007)Google Scholar
  9. 9.
    Rothaus, K., Rhiem, P., Jiang, X.: Separation of the retinal vascular graph in arteries and veins. In: Escolano, F., Vento, M. (eds.) GbRPR 2007. LNCS, vol. 4538, pp. 251–262. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72903-7_23 CrossRefGoogle Scholar
  10. 10.
    Niemeijer, M., van Ginneken, B., Abràmoff, M.D.: Automatic classification of retinal vessels into arteries and veins. In: SPIE Medical Imaging, pp. 72601F–72601F-8 (2009)Google Scholar
  11. 11.
    Vázquez, S.G., Barreira, N., Penedo, M.G., et al.: Improvements in retinal vessel clustering techniques: towards the automatic computation of the arterio venous ratio. Computing 90(3–4), 197–217 (2010)CrossRefzbMATHGoogle Scholar
  12. 12.
    Vázquez, S.G., Barreira, N., Penedo, M.G., Saez, M., Pose-Reino, A.: Using retinex image enhancement to improve the artery/vein classification in retinal images. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010. LNCS, vol. 6112, pp. 50–59. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-13775-4_6 CrossRefGoogle Scholar
  13. 13.
    Relan, D., MacGillivray, T., Ballerini, L., et al.: Retinal vessel classification: sorting arteries and veins. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 7396–7399. IEEE (2013)Google Scholar
  14. 14.
    Joshi, V.S., Reinhardt, J.M., Garvin, M.K., et al.: Automated method for identification and artery-venous classification of vessel trees in retinal vessel networks. PLoS ONE 9(2), e88061 (2014)CrossRefGoogle Scholar
  15. 15.
    Azzopardi, G., Petkov, N.: Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters. Pattern Recogn. Lett. 34(8), 922–933 (2013)CrossRefGoogle Scholar
  16. 16.
    Azzopardi, G., Azzopardi, N.: Trainable COSFIRE filters for keypoint detection and pattern recognition. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 490–503 (2013)CrossRefGoogle Scholar
  17. 17.
    Azzopardi, G., Strisciuglio, N., Vento, M., et al.: Trainable COSFIRE filters for vessel delineation with application to retinal images. Med. Image Anal. 19(1), 46–57 (2015)CrossRefGoogle Scholar
  18. 18.
    Rodieck, R.W.: Quantitative analysis of cat retinal ganglion cell response to visual stimuli. Vis. Res. 5(12), 583–601 (1965)CrossRefGoogle Scholar
  19. 19.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, William M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). doi: 10.1007/BFb0056195 CrossRefGoogle Scholar
  20. 20.
    Yao, C., Chen, H.: Measurement of retinal vessel widths based on prior knowledge. Sci. Pap. 4(1), 64–68 (2009)Google Scholar
  21. 21.
    Han, J.H., Poston, T.: Chord-to-point distance accumulation and planar curvature: a new approach to discrete curvature. Pattern Recogn. Lett. 22(10), 1133–1144 (2001)CrossRefzbMATHGoogle Scholar
  22. 22.
    Shotton, J., Winn, J., Rother, C., Criminisi, A.: TextonBoost: joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006). doi: 10.1007/11744023_1 CrossRefGoogle Scholar
  23. 23.
    Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing features: efficient boosting procedures for multiclass object detection. In: IEEE Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. II-762–II-769 (2004)Google Scholar
  24. 24.
    Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing visual features for multiclass and multiview object detection. IEEE Trans. Pattern Anal. Mach. Intell. 29(5), 854–869 (2007)CrossRefGoogle Scholar
  25. 25.
    Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23, 501–509 (2004)CrossRefGoogle Scholar
  26. 26.
    Estrada, R., Allingham, M.J., Mettu, P.S., et al.: Retinal artery-vein classification via topology estimation. IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015)CrossRefGoogle Scholar
  27. 27.
    Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25, 1200–1213 (2006)CrossRefGoogle Scholar
  28. 28.
    Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26, 1357–1365 (2007)CrossRefGoogle Scholar
  29. 29.
    Mirsharif, Q., Tajeripour, F., Pourreza, H.: Automated characterization of blood vessels as arteries and veins in retinal images. Comput. Med. Imaging Graph. 37(7), 607–617 (2013)CrossRefGoogle Scholar
  30. 30.
    Reza, A.M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J. VLSI Signal Process. 38(1), 35–44 (2004)CrossRefGoogle Scholar
  31. 31.
    Muramatsu, C., Hatanaka, Y., Iwase, T., et al.: Automated detection and classification of major retinal vessels for determination of diameter ratio of arteries and veins. In: SPIE Medical Imaging. International Society for Optics and Photonics, pp. 76240J–76240J-8 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yang Yan
    • 1
    • 2
    • 3
  • Dunwei Wen
    • 2
  • M. Ali Akber Dewan
    • 2
  • Wen-Bo Huang
    • 1
    • 2
    • 3
  1. 1.College of Computer Science and TechnologyChangchun Normal UniversityChangchunChina
  2. 2.School of Computing and Information SystemsAthabasca UniversityAlbertaCanada
  3. 3.College of Communication EngineeringJilin UniversityChangchunChina

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