Analysis of Retinal Vascular Biomarkers for Early Detection of Diabetes

  • Jiong ZhangEmail author
  • Behdad Dashtbozorg
  • Fan Huang
  • Tos T. J. M. Berendschot
  • Bart M. ter Haar Romeny
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 27)


This paper presents an automated retinal vessel analysis system for the measurement and statistical analysis of vascular biomarkers. The proposed retinal vessel enhancement, segmentation, optic disc and fovea detection algorithms provide fundamental tools for extracting the vascular network within the predefined region of interest (ROI). Based on that, the artery/vein classification, vessel caliber, curvature and fractal dimension measurement tools are used to assess the quantitative vascular biomarkers: width, tortuosity, and fractal dimension. A statistical analysis on the extracted geometric biomarkers is set up using a dataset provided by the Maastricht study with the aim of exploring the associations between different vessel biomarkers and type 2 diabetes mellitus. A linear regression analysis is used to model the relationships between different factors. The results indicate that the vascular biomarker variables have associations with diabetes. These findings demonstrate the possibility of applying the proposed pipeline tools on further analysis of vessel biomarkers for the computer-aided diagnosis.


Retinal image analysis Vessel biomarkers Computer-aided diagnosis Diabetes mellitus 



This work is part of the NWO-Hé Programme of Innovation Cooperation No. 629.001.003 and the European Foundation for the Study of Diabetes/Chinese Diabetes Society/Lilly project.


  1. 1.
    Abbasi-Sureshjani, S., Smit-Ockeloen, I., Zhang, J., Romeny, B.T.H.: Biologically-inspired supervised vasculature segmentation in SLO retinal fundus images. In: ICIAR 2015. LNCS, pp. 325–334. Springer, Heidelberg (2015)Google Scholar
  2. 2.
    Abràmoff, M.D., Garvin, M.K., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)CrossRefGoogle Scholar
  3. 3.
    Bekkers, E., Zhang, J., Duits, R., Romeny, B.T.H.: Curvature based biomarkers for diabetic retinopathy via exponential curve fits in SE(2). In: Proceedings of the Ophthalmic Medical Image Analysis Second International Workshop, OMIA 2015 Held in Conjunction with MICCAI, pp. 113–120 (2015)Google Scholar
  4. 4.
    Cheung, C.Y.L., Zheng, Y., Hsu, W., Lee, M.L., Lau, Q.P., Mitchell, P., Wang, J.J., Klein, R., Wong, T.Y.: Retinal vascular tortuosity, blood pressure, and cardiovascular risk factors. Ophthalmology 118(5), 812–818 (2011)CrossRefGoogle Scholar
  5. 5.
    Dashtbozorg, B., Abbasi-Sureshjani, S., Zhang, J., Bekkers, E.J., Huang, F., ter Haar Romeny, B.M.: Infrastructure for retinal image analysis. In: Proceedings of the Ophthalmic Medical Image Analysis Second International Workshop, OMIA 2016, Held in Conjunction with MICCAI 2016, pp. 105–112 (2016)Google Scholar
  6. 6.
    Dashtbozorg, B., Mendonça, A.M., Penas, S., Campilho, A.: Retinacad, a system for the assessment of retinal vascular changes. In: 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 6328–6331. IEEE (2014)Google Scholar
  7. 7.
    Dashtbozorg, B., Zhang, J., Abbasi-Sureshjani, S., Huang, F., ter Haar Romeny, B.M.: Retinal health information and notification system (RHINO). In: Proceedings of SPIE Medical Imaging, vol. 10134, pp. 1013,437–1013,437–6. International Society for Optics and Photonics (2017)Google Scholar
  8. 8.
    Dashtbozorg, B., Zhang, J., Huang, F., ter Haar Romeny, B.M.: Automatic optic disc and fovea detection in retinal images using super-elliptical convergence index filters. In: Campilho, A., Karray, F. (eds.) Image Analysis and Recognition. Lecture Notes in Computer Science, vol. 9730, pp. 697–706. Springer (2016)Google Scholar
  9. 9.
    Dashtzbozorg, B., Mendonca, M.A., Penas, S., Campilho, A.: Computer-aided diagnosis system for the assessment of retinal vascular changes. In: Proceedings of the Ophthalmic Medical Image Analysis First International Workshop, OMIA 2014, Held in Conjunction with MICCAI 2014, pp. 9–16 (2014)Google Scholar
  10. 10.
    ter Haar Romeny, B.M., Bekkers, E.J., Zhang, J., Abbasi-Sureshjani, S., Huang, F., Duits, R., Dashtbozorg, B., Berendschot, T.T., Smit-Ockeloen, I., Eppenhof, K.A., et al.: Brain-inspired algorithms for retinal image analysis. Mach. Vis. Appl. 27(8), 1117–1135 (2016)CrossRefGoogle Scholar
  11. 11.
    Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imag. 19(3), 203–210 (2000)CrossRefGoogle Scholar
  12. 12.
    Huang, F., Dashtbozorg, B., Zhang, J., Bekkers, E., Abbasi-Sureshjani, S., Berendschot, T.T., ter Haar Romeny, B.M.: Reliability of using retinal vascular fractal dimension as a biomarker in the diabetic retinopathy detection. J. Ophthalmol. 2016(6259047), 13 (2016)Google Scholar
  13. 13.
    Huang, F., Zhang, J., Bekkers, E., Dashtbozorg, B., ter Haar Romeny, B.: Stability analysis of fractal dimension in retinal vasculature. In: Proceedings of the Ophthalmic Medical Image Analysis Second International Workshop, OMIA 2015 Held in Conjunction with MICCAI 2015, pp. 1–8 (2015)Google Scholar
  14. 14.
    MESSIDOR, T.V.: Messidor: methods to evaluate segmentation and indexing techniques in the field of retinal ophthalmology (2014). Accessed 9 Oct 2014
  15. 15.
    Ortega, M., Barreira, N., Novo, J., Penedo, M.G., Pose-Reino, A., Gómez-Ulla, F.: Sirius: a web-based system for retinal image analysis. Int. J. Med. Inform. 79(10), 722–732 (2010)CrossRefGoogle Scholar
  16. 16.
    Schram, M.T., Sep, S.J., van der Kallen, C.J., Dagnelie, P.C., Koster, A., Schaper, N., Henry, R.M., Stehouwer, C.D.: The Maastricht study: an extensive phenotyping study on determinants of type 2 diabetes, its complications and its comorbidities. Eur. J. Epidemiol. 29(6), 439–451 (2014)CrossRefGoogle Scholar
  17. 17.
    Staal, J., Abràmoff, M., Niemeijer, M., Viergever, M., Van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imag. 23(4), 501–509 (2004)CrossRefGoogle Scholar
  18. 18.
    Wong, T.Y., Islam, F.A., Klein, R., Klein, B.E., Cotch, M.F., Castro, C., Sharrett, A.R., Shahar, E.: Retinal vascular caliber, cardiovascular risk factors, and inflammation: the multi-ethnic study of atherosclerosis (MESA). Invest. Ophthalmol. Vis. Sci. 47(6), 2341–2350 (2006)CrossRefGoogle Scholar
  19. 19.
    Zhang, J., Bekkers, E., Abbasi, S., Dashtbozorg, B., ter Haar Romeny, B.: Robust and fast vessel segmentation via Gaussian derivatives in orientation scores. In: Murino, V., Puppo, E. (eds.) Image Analysis and Processing. Lecture Notes in Computer Science, vol. 9279, pp. 537–547. Springer (2015)Google Scholar
  20. 20.
    Zhang, J., Dashtbozorg, B., Bekkers, E., Pluim, J.P.W., Duits, R., ter Haar Romeny, B.M.: Robust retinal vessel segmentation via locally adaptive derivative frames in orientation scores. IEEE Trans. Med. Imaging 35(12), 2631–2644 (2016)CrossRefGoogle Scholar
  21. 21.
    Zhang, J., Duits, R., Sanguinetti, G., ter Haar Romeny, B.M.: Numerical approaches for linear left-invariant diffusions on SE(2), their comparison to exact solutions, and their applications in retinal imaging. Numer. Math. Theor. Meth. Appl. 9(1), 1–50 (2016)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jiong Zhang
    • 1
    Email author
  • Behdad Dashtbozorg
    • 1
  • Fan Huang
    • 1
  • Tos T. J. M. Berendschot
    • 2
  • Bart M. ter Haar Romeny
    • 1
    • 3
  1. 1.Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.University Eye Clinic MaastrichtMaastrichtThe Netherlands
  3. 3.Department of Biomedical and Information EngineeringNortheastern UniversityShenyangChina

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