Skip to main content

A new combined method based on curvelet transform and morphological operators for automatic detection of foveal avascular zone


In order to achieve early detection of diabetic retinopathy (DR) for the sake of preventing from blindness, regular screening using retinal photography is necessary. Abnormalities of DR do not have uniform distribution over the retina. Certain types of abnormalities usually occur in specific areas on the retina. The distance between lesions, such as micro-aneurysms, and the foveal avascular zone (FAZ) is a useful feature for later analysis and grading of DR. In this paper, a new fully automatic system is presented to find the location of FAZ in fundus fluorescein angiogram photographs. The method is based on two procedures: digital curvelet transform (DCUT) and morphological operations. Firstly, end points of vessels are detected based on vessel segmentation using DCUT. By connecting these points in the selected region of interest, FAZ region is extracted. Secondly, vessels are subtracted from the retinal image, and morphological dilatation and erosion are applied on the resulted image. By choosing an appropriate threshold, FAZ region is detected. The final FAZ region is extracted by performing logical AND between two segmented FAZ. Our experiments show that the system achieves, respectively, the specificity and sensitivity of (>98 and >96 %) for normal stage, for mild/moderate non-proliferative DR (NPDR) (>98, and >95 %) and for Sever NPDR + PDR (>97 and >93 %).

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15


  1. 1.

    Tobin, K.W., Chaum, S.E., Govindasamy, V.P., Karnowski, ThP: Detection of anatomic structures in human retinal imagery. IEEE Trans. Med. Imaging 26, 1729–1739 (2007)

    Article  Google Scholar 

  2. 2.

    Niemeijer, M., Abramoff, M.D.: Segmentation of the optic disk, macula and vascular arch in fundus photographs. IEEE Trans. Med. Imaging 26, 116–127 (2007)

    Article  Google Scholar 

  3. 3.

    Hiuiqi, L.: Automated feature extraction in color retinal images by a model based approach. IEEE Trans. Biomed. Eng. 51, 246–254 (2004)

    Article  Google Scholar 

  4. 4.

    Walter, T., Massin, P., Erginay, A., Ordonez, R., Jeulin, C., Klein, J.C.: Automatic detection of microaneurysms in color fundus images. Med. Image Anal. 11(6), 555–566 (2007)

    Article  Google Scholar 

  5. 5.

    Walter, T., Klein, J.C., Massin, P., Erginay, A.: A contribution of image processing to the diagnosis of diabetic retinopathy—detection of exudates in color fundus images of the human retina. IEEE Trans. Med. Imaging 21(10), 1236–1243 (2002)

    Article  Google Scholar 

  6. 6.

    Walter, T., Klein, J.C.: Segmentation of color fundus images of the human retina: detection of the optic disc and the vascular tree using morphological techniques. ISMDA 2001, 282–287 (2001)

    Google Scholar 

  7. 7.

    Abràmoff, M.D., Garvin, M., Sonka, M.: Retinal imaging and image analysis. IEEE Rev. Biomed. Eng. 3, 169–208 (2010)

    Article  Google Scholar 

  8. 8.

    Esmaeili, M., Rabbani, H., Dehnavi, A.M.: Automatic optic disk boundary extraction by the use of curvelet transform and deformable variational level set model. Pattern Recognit. 45(7), 2832–2842 (2012)

    Article  MathSciNet  Google Scholar 

  9. 9.

    Zana, F., Klein, J.C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Image Process. 10(7), 1010–1019 (2001)

    Article  MATH  Google Scholar 

  10. 10.

    Zana, F., Klein, J.C.: A multi-modal registration algorithm of eye fundus images using vessels detection and Hough transform. IEEE Trans. Med. Imaging 18(5), 419–428 (1999)

    Article  Google Scholar 

  11. 11.

    Lee, Sangyeol, Reinhardt, Joseph M., Cattin, Philippe C., Abràmoff, Michael D.: Objective and expert-independent validation of retinal image registration algorithms by a projective imaging distortion model. Med. Image Anal. 14(4), 539–549 (2010)

    Article  Google Scholar 

  12. 12.

    Patton, N., Aslam, T.M., MacGillivray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., Yogesan, K., Constable, I.J.: Retinal image analysis: concepts, applications and potential. Prog. Retin. Eye Res. 25(1), 99–127 (2006)

    Article  Google Scholar 

  13. 13.

    Tsai, C.L., Madore, B., Leotta, M.J., Sofka, M., Yang, G., Majerovics, A., Tanenbaum, H.L., Stewart, C.V., Roysam, B.: Automated retinal image analysis over the internet. IEEE Trans. Inf. Technol. Biomed. 12(4), 480–487 (2008)

    Article  Google Scholar 

  14. 14.

    Ahmed, M.I., Amin, M.A.: High speed detection of optical disc in retinal fundus image. Signal Image Video Processing. doi:10.1007/s11760-012-0412-3

  15. 15.

    Nirmala, S.R., Dandapat, S., Bora, P.K.: Wavelet weighted distortion measure for retinal images. Signal Image Video Processing. doi:10.1007/s11760-012-0290-8

  16. 16.

    Niemeijer, M., Abramoff, M.D., Ginneken, B.V.: Fast detection of the optic disc and fovea in color fundus photographs. Med. Image Anal. 13, 859–870 (2009)

    Article  Google Scholar 

  17. 17.

    Haddouche, A., Adel, M., Rasigni, M., Conrath, J., Bourennanea, S.: Detection of the foveal avascular zone on retinal angiograms using Markov random fields. Digit. Signal Process. 20, 149–154 (2010)

    Article  Google Scholar 

  18. 18.

    Regillo, C.D.: 2007–2008 Basic and clinical science course Section 12: retina and vitreous. American Academy of Ophthalmology. Accessed 7 Dec 2011

  19. 19.

    Kovacs, L., Qureshi, R.J., Nagy, B., Harangi, B. Hajdu, A.: Graph based detection of optic disc and fovea in retinal image. In: IEEE International Workshop on Soft Computing Applications, pp. 143–148 (2010)

  20. 20.

    Tobin, K.W.: Detection of anatomic structures in human retinal imagery. IEEE Trans. Med. Imaging 26, 1729–1739 (2007)

    Article  Google Scholar 

  21. 21.

    Sekhar, S., Nuaimy, W.Al., Nandi, A.K.: Automated localization of optic disc and fovea in retinal fundus images. In: Proceedings of 16th European Signal Processing Conference, 5 pages, Lausanne, Switzerland (2008)

  22. 22.

    Tan, N.M., Wong, D.W.K., Liu, J., Ng, W.J., Zhang, Z., Lim, J.H., Tan, Z., Tang, Y., Li, H., Lu, S., Wong, T.Y.: Automatic detection of the macula in the retinal fundus image by detecting regions with low pixel intensity. In: IEEE Biomedical and Pharmaceutical Engineering, pp. 1–5 (2009)

  23. 23.

    Gutirrez, J., Epifanio, I., DeVes, E., Fed, F.J.: An active contour model for the automatic detection of the fovea in fluorescein angiographies. In: IEEE International Conference on Pattern Recognition, pp. 312–315 (2000)

  24. 24.

    Zana, F., Meunier, I., Klein, J.C.: A region merging algorithm using mathematical morphology: application to macula detection. In: International Symposium on Mathematical Morphology and its Applications to Image and Signal Processing, pp. 423–430 (1998)

  25. 25.

    Fleming, A.D., Philip, S., Goatman, K.A., Olson, J.A., Sharp, P.F.: Automated assessment of diabetic retinal image quality based on clarity and field definition. Invest Ophthalmol. Vis. Sci. 47, 1120–1125 (2006)

    Article  Google Scholar 

  26. 26.

    Goldberg, R.E., Varma, R., Spaeth, G.L., Magargal, L.E., Callen, D.: Quantification of progressive diabetic macular nonperfusion. Ophthalmic Surg. 20, 42–45 (1989)

    Google Scholar 

  27. 27.

    Early Treatment Diabetic Retinopathy Study Research Group: Classification of diabetic retinopathy from fluorescein angiograms. ETDRS report number 11. Ophthalmology 98, 807–822 (1991)

    Google Scholar 

  28. 28.

    Phillips, R.P., Spencer, T., Ross, P.G., Sharp, P.F., Forrester, J.V.: Quantification of diabetic maculopathy by digital imaging of the fundus. Eye 5, 130–137 (1991)

    Article  Google Scholar 

  29. 29.

    Conrath, J., Giorgi, R., Raccah, D., Ridings, B.: Foveal avascular zone in diabetic retinopathy: quantitative vs qualitative assessment. Eye 19, 322–326 (2004)

    Article  Google Scholar 

  30. 30.

    Conrath, J., Valat, O., Giorgi, R., et al.: Semi-automated detection of the foveal avascular zone in fluorescein angiograms in diabetes mellitus. Clin. Exp. Ophthalmol. 34, 119–123 (2006)

    Article  Google Scholar 

  31. 31.

    Zheng, Y., Gandhi, J.S., Stangos, A.N., Campa, C., Broadbent, D.M., Harding, S.P.: Automated segmentation of foveal avascular zone in fundus fluorescein angiography. Invest Ophthalmol. Vis. Sci. 51, 3653–3659 (2010)

    Article  Google Scholar 

  32. 32.

    Popovic, Z., Knutsson, P., Thaung, J., Owner-Petersen, M., Sjöstrand, J.: Noninvasive imaging of human foveal capillary network using dual-conjugate adaptive optics. Invest Ophthalmol. Vis. Sci. 52, 2649–2655 (2011)

    Article  Google Scholar 

  33. 33.

    Martin, J.A., Roorda, A.: Direct and noninvasive assessment of parafoveal capillary leukocyte velocity. Ophthalmology 112, 2219–2224 (2005)

    Article  Google Scholar 

  34. 34.

    Tam, J., Martin, J.A., Roorda, A.: Noninvasive visualization and analysis of parafoveal capillaries in humans. Invest Ophthalmol. Vis. Sci. 51, 1691–1698 (2010)

    Article  Google Scholar 

  35. 35.

    Shin, Y.U., Kim, S., Lee, B.R., Shin, J.W., Kim, S.I.: Novel noninvasive detection of the fovea avascular zone using confocal red-free imaging in diabetic retinopathy and retinal vein occlusion. Invest Ophthalmol. Vis. Sci. 53(1), 309–315 (2012)

    Article  Google Scholar 

  36. 36.

    Ballerini, L.: Genetic snakes for medical images segmentation. Math Model. Estim. Tech. Comput. Vis. 3457, 284–295 (1998)

    Article  Google Scholar 

  37. 37.

    Ibañez, M.V., Simó, A.: Bayesian detection of the fovea in eye fundus angiographies. Pattern Recognit. Lett. 20, 229–240 (1999)

    Article  MATH  Google Scholar 

  38. 38.

    Petsatodis, T., Diamantis, A., Syrcos, G.P.: A complete algorithm for automatic human recognition based on retina vascular network characteristics. In: 1st International Scientific Conference e RA, Tripolis, Greece, pp. 41–46 (2004)

  39. 39.

    Sinthanayothin, C., Boyce, J.F., Cook, H.L., Williamson, T.H.: Automated localization of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. Br. J. Ophthalmol. 83(8), 902–910 (1999)

    Article  Google Scholar 

  40. 40.

    Fleming, A.D., Goatman, K.A., Philip, S., Olson, J.A., Sharp, P.F.: Automatic detection of retinal anatomy to assist diabetic retinopathy screening. Phys. Med. Biol. 52(2), 331–345 (2007)

    Google Scholar 

  41. 41.

    Li, H., Chutatape, O.: Automated feature extraction in color retinal images by a model based approach. IEEE Trans. Biomed. Eng. 51(2), 246–254 (2004)

    Article  Google Scholar 

  42. 42.

    Osareh, A., Mirmehdi, M., Thomas, B., Markham, R.: Comparison of colour spaces for optic disc localisation in retinal images. In: Proceedings of the 16th International Conference on Pattern Recognition (ICPR’02), vol. 1, pp. 743–746 (2002)

  43. 43.

    Lalonde, M., Beaulieu, M., Gagnon, L.: Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching. IEEE Trans. Med. Imaging 20(11), 1193–1200 (2001)

    Article  Google Scholar 

  44. 44.

    Youssif, A., Ghalwash, A., Ghoneim, A.: Optic disc detection from normalized digital fundus images by means of a vessels’ direction matched filter. IEEE Trans. Med. Imaging 27(1), 11–18 (2008)

    Google Scholar 

  45. 45.

    Starck, J.-L., Murtagh, F., Candès, E.J., Donoho, D.L.: Gray and color image contrast enhancement by the curvelet transform. IEEE Trans. Image Process. 12, 706–717 (2003)

    Google Scholar 

  46. 46.

    Pisano, E., Zong, S., Heminger, B., Deluca, M., Johnston, R., Muller, K., Breauning, M.P., Pizer, S.M.: Contrast limited adaptive histogram equalization image processing to improve the detection of simulated speculations in dense mammograms. Digit. Imaging 11, 193–200 (1998)

    Google Scholar 

  47. 47.

    Aibinu, A.M., Salami, M.J.E., Shfie, A.A.: Retina fundus image mask generation using pseudo parametric modeling technique. IIUM Eng. J. 11, 163–177 (2010)

    Google Scholar 

  48. 48.

    Candès, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms, multiscale model. Simulation 5, 861–899 (2006)

    MATH  Google Scholar 

  49. 49.

    Esmaeili, M., Rabbani, H., Mehri Dehnavi, A.R., Dehghani, A.R.: Automatic optic disk detection by the use of curvelet transform. In: IEEE International Conference on Information Technology and Applications in Biomedicine, pp. 1–4

  50. 50.

    Esmaeili, M., Rabbani, H., Mehri Dehnavi, A.R., Dehghani, A.R.: Extraction of retinal blood vessels by curvelet transform. In: IEEE International Conference on Image Processing, pp. 3353–3356 (2009)

  51. 51.

    Hajeb, S.H., Rabbani, H., Akhlaghi, M.: Diabetic retinopathy grading by digital curvelet transform. Computational and Mathematical Methods in Medicine, vol. 2012, Article ID 761901, 11 pages (2012)

  52. 52.

    Fadzil, M.H.A., Nugroho, H., Izhar, L.I., Nugroho, H.A.: Analysis of retinal fundus images for grading of diabetic retinopathy severity. Med. Biol. Eng. Comput. 49, 693–700 (2010)

    Article  Google Scholar 

  53. 53.

Download references

Author information



Corresponding author

Correspondence to Hossein Rabbani.



In order to show an estimation of the interobserver variability of proposed procedure in this paper for FAZ detection and intra-observer too, the overlap between segmentations for all ground truth images in this study is showed in Table 4 that could be compared directly to the quantitative overlap index reported in Table 1 for checking the lack of outlier cases.

Table 4 Overlapping ratio between results of morphological-based and curvelet-based methods, and inter- and intra-observer overlapping ratio for FFA images in this study

In order to group the results for each subpopulation of DR (normal, Mild/Moderate NPDR and Severe NPDR + PDR), the overlapping ratio, specificity and sensitivity of all data in each group can be seen in Fig. 16.

Fig. 16

The plot of overlapping ratio, specificity and sensitivity of our FAZ detection method for each patient in each group (normal, mild/moderate NPDR and Sever NPDR + PDR)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Hajeb Mohammad Alipour, S., Rabbani, H. & Akhlaghi, M. A new combined method based on curvelet transform and morphological operators for automatic detection of foveal avascular zone. SIViP 8, 205–222 (2014).

Download citation


  • Diabetic retinopathy (DR)
  • Foveal avascular zone (FAZ )
  • Fundus fluorescein angiogram
  • Digital curvelet transform (DCUT)
  • Morphological operations