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International Conference of Pioneering Computer Scientists, Engineers and Educators

ICYCSEE 2016: Social Computing pp 228–237Cite as

Automated Artery-Vein Classification in Fundus Color Images

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 623))

Abstract

The estimation of Arterio-Venous ratio (AVR) is an important phase in diagnosing various vascular diseases e.g. Diabetic Retinopathy. For calculating this value, it is essential to differentiate the vessels into arteries and veins. This paper presents a novel structural and automated method for artery/vein vessels classification in retinal images. Our method is tested on DRIVE database and the classification accuracy is 88.7 % for pixels and 89.07 % for vessel lines, respectively, which demonstrate the effectives of our approach. Our method will help to achieve the fundus disease surveillance on mobile and remote medical treatment. It has a remarkable social significance.

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References

  1. Grisan, E., Ruggeri, A.: 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 on Engineering in Medicine and Biology Society, vol. 1, pp. 890–893. IEEE (2003)

    Google Scholar 

  2. 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 

  3. Relan, D., MacGillivray, T., Ballerini, L., et al.: Retinal vessel classification: sorting arteries and veins. In: 35th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society (EMBC), pp. 7396–7399. IEEE (2013)

    Google Scholar 

  4. Vázquez, S.G., Cancela, B., Barreira, N., et al.: Improving retinal artery and vein classification by means of a minimal path approach. Mach. Vis. Appl. 24(5), 919–930 (2013)

    Article  Google Scholar 

  5. 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)

    Article  Google Scholar 

  6. 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)

    Article  MATH  Google Scholar 

  7. 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)

    Article  Google Scholar 

  8. Mirsharif, Q., Tajeripour, F.: Investigating image enhancement methods for better classification of retinal blood vessels into arteries and veins. In: 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), pp. 591–597 (2012)

    Google Scholar 

  9. Staal, J., Abràmoff, M.D., Niemeijer, M., et al.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)

    Article  Google Scholar 

  10. Jobson, D.J., Rahman, Z., Woodell, G.A.: Properties and performance of a center/surround retinex. IEEE Trans. Image Process. 6(3), 451–462 (1997)

    Article  Google Scholar 

  11. Jobson, D.J., Rahman, Z., Woodell, G.A.: A multiscale retinex for bridging the gap between color images and the human observation of scenes. IEEE Trans. Image Process. 6(7), 965–976 (1997)

    Article  Google Scholar 

  12. 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)

    Article  Google Scholar 

  13. Usman Akram, M., Khan, A., Iqbal, K., Butt, W.H.: Retinal images: optic disk localization and detection. In: Campilho, A., Kamel, M. (eds.) ICIAR 2010, Part II. LNCS, vol. 6112, pp. 40–49. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. Zamperini, A., Giachetti, A., Trucco, E., et al.: Effective features for artery-vein classification in digital fundus images. In: 2012 25th International Symposium on Computer-Based Medical Systems (CBMS), pp. 1–6. IEEE (2012)

    Google Scholar 

  16. Ruggeri, A., Grisan, E., De Luca, M.: An automatic system for the estimation of generalized arteriolar narrowing in retinal images. In: 29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, EMBS 2007, pp. 6463–6466 (2007)

    Google Scholar 

  17. Irshad, S., Akram, M.U.: Classification of retinal vessels into arteries and veins for detection of hypertensive retinopathy. In: 2014 Cairo International Biomedical Engineering Conference (CIBEC), pp. 133–136. IEEE (2014)

    Google Scholar 

  18. 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. 76240 J–76240 J-8 (2010)

    Google Scholar 

  19. Niemeijer, M., van Ginneken, B., Abràmoff, M.D.: Automatic classification of retinal vessels into arteries and veins. In: SPIE medical imaging. International Society for Optics and Photonics, pp. 72601F–72601F-8 (2010)

    Google Scholar 

  20. Calvo, D., Ortega, M., Penedo, M.G., et al.: Automatic detection and characterisation of retinal vessel tree bifurcations and crossovers in eye fundus images. Comput. Methods Programs Biomed. 103(1), 28–38 (2011)

    Article  Google Scholar 

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Acknowledgment

This work was supported in part by the Natural Science Foundation of China under Grant 61472102, in part by the Fundamental Research Funds for the Central Universities under Grant HIT.NSRIF.2013091, and in part by the Humanity and Social Science Youth foundation of Ministry of Education of China under Grant 14YJC760001.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Yi Yang, Kuanquan Wang & Xiangqian Wu

  2. Department of New Media Technologies and Arts, Harbin Institute of Technology, Harbin, 150001, China

    Wei Bu

  3. Department of Eye and Vision Science, University of Liverpool, Liverpool, L7 8TX, UK

    Yalin Zheng

Authors
  1. Yi Yang
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  2. Wei Bu
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  3. Kuanquan Wang
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  4. Yalin Zheng
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  5. Xiangqian Wu
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Corresponding author

Correspondence to Xiangqian Wu .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology , Harbin, China

    Wanxiang Che

  2. Harbin Engineering University , Harbin, China

    Qilong Han

  3. Harbin Institute of Technology , Harbin, China

    Hongzhi Wang

  4. Northeast Forestry University , Harbin, China

    Weipeng Jing

  5. National University of Defense Technology , Changsha, China

    Shaoliang Peng

  6. Harbin Engineering University , Harbin, China

    Junyu Lin

  7. Harbin Univ. of Science and Technology , Harbin, China

    Guanglu Sun

  8. Harbin Univ. of Science and Technology , Harbin, China

    Xianhua Song

  9. Harbin Engineering University , Harbin, China

    Hongtao Song

  10. Harbin Sea of Clouds & Computer Tech. , Harbin, China

    Zeguang Lu

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© 2016 Springer Science+Business Media Singapore

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Yang, Y., Bu, W., Wang, K., Zheng, Y., Wu, X. (2016). Automated Artery-Vein Classification in Fundus Color Images. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_21

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  • DOI: https://doi.org/10.1007/978-981-10-2053-7_21

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2052-0

  • Online ISBN: 978-981-10-2053-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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