Abstract
The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents a new approach to solve this problem by means of a Fully-Connected Convolutional Neural Network that is specifically adapted for artery/vein classification. For this, a loss function that focuses only on pixels belonging to the retinal vessel tree is built. The relevance of providing the model with different chromatic components of the source images is also analyzed. The performance of the proposed method is evaluated on the RITE dataset of retinal images, achieving promising results, with an accuracy of \(96\%\) on large caliber vessels, and an overall accuracy of \(84\%\).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Costa, P., Campilho, A.: Convolutional bag of words for diabetic retinopathy detection from eye fundus images. IPSJ Trans. Comput. Vis. Appl. 9(1), 10 (2017)
Dashtbozorg, B., Mendonça, A.M., Campilho, A.: An automatic method for the estimation of arteriolar-to-venular ratio in retinal images. In: IEEE International Symposium on Computer-Based Medical Systems, pp. 512–513, June 2013
Dashtbozorg, B., Mendonça, A.M., Campilho, A.: Optic disc segmentation using the sliding band filter. Comput. Biol. Med. 56, 1–12 (2015)
Dashtbozorg, B., Mendonca, A.M., Campilho, A.: An automatic graph-based approach for artery/vein classification in retinal images. IEEE Trans. Image Process. 23(3), 1073–1083 (2014)
Estrada, R., Allingham, M.J., Mettu, P.S., Cousins, S.W., Tomasi, C., Farsiu, S.: Retinal artery-vein classification via topology estimation. IEEE Trans. Med. Imaging 34(12), 2518–2534 (2015)
Hu, Q., Abràmoff, M.D., Garvin, M.K.: Automated separation of binary overlapping trees in low-contrast color retinal images. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 436–443. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40763-5_54
Ikram, M.K., de Jong, F.J., Vingerling, J.R., Witteman, J.C.M., Hofman, A., Breteler, M.M.B., de Jong, P.T.V.M.: Are retinal arteriolar or venular diameters associated with markers for cardiovascular disorders? The rotterdam study. Invest. Ophthalmol. Vis. Sci. 45(7), 2129–2134 (2004)
Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations, pp. 1–13 (2014)
Kondermann, C., Kondermann, D., Yan, M.: Blood vessel classification into arteries and veins in retinal images. In: Image Processing, p. 651247 (2007)
Meyer, M.I., Costa, P., Galdran, A., Mendonça, A.M., Campilho, A.: A deep neural network for vessel segmentation of scanning laser ophthalmoscopy images. In: Karray, F., Campilho, A., Cheriet, F. (eds.) ICIAR 2017. LNCS, vol. 10317, pp. 507–515. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59876-5_56
Nguyen, T.T., Wong, T.Y.: Retinal vascular changes and diabetic retinopathy. Curr. Diabetes Rep. 9(4), 277–283 (2009)
Niemeijer, M., van Ginneken, B., Abramoff, M.D.: Automatic classification of retinal vessels into arteries and veins. In: SPIE medical imaging. International Society for Optics and Photonics, November 2016, vol. 7260, pp. 1–8 (2009)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Rothaus, K., Jiang, X., Rhiem, P.: Separation of the retinal vascular graph in arteries and veins based upon structural knowledge. Image Vis. Comput. 27(7), 864–875 (2009)
Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., Ginneken, B.V.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)
Welikala, R., Foster, P., Whincup, P., Rudnicka, A., Owen, C., Strachan, D., Barman, S.: Automated arteriole and venule classification using deep learning for retinal images from the UK biobank cohort. Comput. Biol. Med. 90, 23–32 (2017)
Yu, H., Barriga, S., Agurto, C., Nemeth, S., Bauman, W., Soliz, P.: Automated Retinal Vessel Type Classification in Color Fundus Images, vol. 8670 (2013)
Zamperini, A., Giachetti, A., Trucco, E., Chin, K.S.: Effective features for artery-vein classification in digital fundus images. In: Proceedings - IEEE Symposium on Computer-Based Medical Systems (2012)
Acknowledgments
This work is funded by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and the European Regional Development Fund (ERDF), within the project “NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Meyer, M.I., Galdran, A., Costa, P., Mendonça, A.M., Campilho, A. (2018). Deep Convolutional Artery/Vein Classification of Retinal Vessels. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_71
Download citation
DOI: https://doi.org/10.1007/978-3-319-93000-8_71
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-92999-6
Online ISBN: 978-3-319-93000-8
eBook Packages: Computer ScienceComputer Science (R0)