Learning Fully-Connected CRFs for Blood Vessel Segmentation in Retinal Images

  • José Ignacio Orlando
  • Matthew Blaschko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


In this work, we present a novel method for blood vessel segmentation in fundus images based on a discriminatively trained, fully connected conditional random field model. Retinal image analysis is greatly aided by blood vessel segmentation as the vessel structure may be considered both a key source of signal, e.g. in the diagnosis of diabetic retinopathy, or a nuisance, e.g. in the analysis of pigment epithelium or choroid related abnormalities. Blood vessel segmentation in fundus images has been considered extensively in the literature, but remains a challenge largely due to the desired structures being thin and elongated, a setting that performs particularly poorly using standard segmentation priors such as a Potts model or total variation. In this work, we overcome this difficulty using a discriminatively trained conditional random field model with more expressive potentials. In particular, we employ recent results enabling extremely fast inference in a fully connected model. We find that this rich but computationally efficient model family, combined with principled discriminative training based on a structured output support vector machine yields a fully automated system that achieves results statistically indistinguishable from an expert human annotator. Implementation details are available at matthew.blaschko/projects/retina/.


Blood vessel segmentation Fundus imaging Conditional Random Fields Structured Output SVM 


  1. 1.
    Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Blood vessel segmentation methodologies in retinal images–a survey. Computer Methods and Programs in Biomedicine 108(1), 407–433 (2012)CrossRefGoogle Scholar
  2. 2.
    Miri, M.S., Mahloojifar, A.: Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE T-BME 58(5), 1183–1192 (2011)CrossRefGoogle Scholar
  3. 3.
    Kanski, J.J., Bowling, B.: Synopsis of Clinical Ophthalmology. Saunders Limited (2012)Google Scholar
  4. 4.
    Li, Y., Gregori, G., Knighton, R.W., Lujan, B.J., Rosenfeld, P.J.: Registration of OCT fundus images with color fundus photographs based on blood vessel ridges. Optics Express 19(1), 7 (2011)CrossRefGoogle Scholar
  5. 5.
    Ricci, E., Perfetti, R.: Retinal blood vessel segmentation using line operators and support vector classification. IEEE T-MI 26(10), 1357–1365 (2007)CrossRefGoogle Scholar
  6. 6.
    Becker, C., Rigamonti, R., Lepetit, V., Fua, P.: Supervised feature learning for curvilinear structure segmentation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 526–533. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Mendonca, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE T-MI 25(9) (2006)Google Scholar
  8. 8.
    Martinez-Perez, M.E., Hughes, A.D., Thom, S.A., Bharath, A.A., Parker, K.H.: Segmentation of blood vessels from red-free and fluorescein retinal images. Medical Image Analysis 11(1), 47–61 (2007)CrossRefGoogle Scholar
  9. 9.
    Zhang, B., Zhang, L., Zhang, L., Karray, F.: Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Computers in Biology and Medicine 40(4), 438–445 (2010)CrossRefGoogle Scholar
  10. 10.
    Nguyen, U.T., Bhuiyan, A., Park, L.A., Ramamohanarao, K.: An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognition (2012)Google Scholar
  11. 11.
    Fraz, M.M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A.R., Owen, C.G., Barman, S.A.: Ensemble classification system applied for retinal vessel segmentation on child images containing various vessel profiles. Image Analysis and Recognition (2012)Google Scholar
  12. 12.
    Vlachos, M., Dermatas, E.: Multi-scale retinal vessel segmentation using line tracking. Computerized Medical Imaging and Graphics 34(3), 213–227 (2010)CrossRefGoogle Scholar
  13. 13.
    Li, S.Z.: Markov Random Field Modeling in Image Analysis, 3rd edn. Springer (2009)Google Scholar
  14. 14.
    Krähenbühl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: NIPS (2012)Google Scholar
  15. 15.
    Joachims, T., Finley, T., Yu, C.N.J.: Cutting-plane training of structural SVMs. Machine Learning 77(1), 27–59 (2009)CrossRefzbMATHGoogle Scholar
  16. 16.
    Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge based vessel segmentation in color images of the retina. IEEE T-MI 23(4), 501–509 (2004)CrossRefGoogle Scholar
  17. 17.
    Schölkopf, B.: Support Vector Learning. PhD thesis, Oldenbourg Verlag, Munich (1997)Google Scholar
  18. 18.
    Soares, J.V., Leandro, J.J., Cesar, R.M., Jelinek, H.F., Cree, M.J.: Retinal vessel segmentation using the 2-d Gabor wavelet and supervised classification. IEEE T-MI 25(9) (2006)Google Scholar
  19. 19.
    Al-Rawi, M., Qutaishat, M., Arrar, M.: An improved matched filter for blood vessel detection of digital retinal images. Computers in Biology and Medicine 37(2), 262–267 (2007)CrossRefGoogle Scholar
  20. 20.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  21. 21.
    Marín, D., Aquino, A., Gegúndez-Arias, M.E., Bravo, J.M.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE T-MI 30(1), 146–158 (2011)CrossRefGoogle Scholar
  22. 22.
    Sinthanayothin, C., Boyce, J.F., Cook, H.L., Williamson, T.H.: Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. British Journal of Ophthalmology 83(8), 902–910 (1999)CrossRefGoogle Scholar
  23. 23.
    Saleh, M.D., Eswaran, C.: An efficient algorithm for retinal blood vessel segmentation using h-maxima transform and multilevel thresholding. Computer Methods in Biomechanics and Biomedical Engineering 15(5), 517–525 (2012)CrossRefGoogle Scholar
  24. 24.
    Zana, F., Klein, J.-C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE TIP 10(7), 1010–1019 (2001)zbMATHGoogle Scholar
  25. 25.
    You, X., Peng, Q., Yuan, Y., Cheung, Y.-M., Lei, J.: Segmentation of retinal blood vessels using the radial projection and semi-supervised approach. Pattern Recognition 44(10) (2011)Google Scholar
  26. 26.
    Palomera-Pérez, M.A., Martinez-Perez, M.E., Benítez-Pérez, H., Ortega-Arjona, J.L.: Parallel multiscale feature extraction and region growing: application in retinal blood vessel detection. IEEE T-ITB 14(2), 500–506 (2010)Google Scholar
  27. 27.
    Al-Diri, B., Hunter, A., Steel, D.: An active contour model for segmenting and measuring retinal vessels. IEEE T-MI 28(9), 1488–1497 (2009)CrossRefGoogle Scholar
  28. 28.
    Espona, L., Carreira, M.J., Penedo, M.G., Ortega, M.: Retinal vessel tree segmentation using a deformable contour model. In: ICPR (2008)Google Scholar
  29. 29.
    Espona, L., Carreira, M.J., Ortega, M., Penedo, M.G.: A snake for retinal vessel segmentation. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds.) IbPRIA 2007. LNCS, vol. 4478, pp. 178–185. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • José Ignacio Orlando
    • 1
    • 2
    • 3
  • Matthew Blaschko
    • 1
    • 4
  1. 1.Équipe GalenINRIA SaclayÎle-de-FranceFrance
  2. 2.Consejo Nacional de Investigaciones Científicas y TécnicasCONICETArgentina
  3. 3.Pladema InstituteUNCPBAArgentina
  4. 4.Center for Learning and Visual ComputingÉcole Centrale ParisFrance

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