Simulation of Diabetic Retinopathy Neovascularization in Color Digital Fundus Images

  • Xinyu Xu
  • Baoxin Li
  • Jose F. Florez
  • Helen K. Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4291)


Diabetic retinopathy (DR) has been identified as a leading cause of blindness. One type of lesion, neovascularization (NV), indicates that the disease has entered a vision-threatening phase. Early detection of NV is thus clinically significant. Efforts have been devoted to use computer-aided analyses of digital retina images to detect DR. However, developing reliable NV detection algorithms requires large numbers of digital retinal images to test and refine approaches. Computer simulation of NV offers the potential of developing lesion detection algorithms without the need for large image databases of real pathology. In this paper, we propose a systematic approach to simulating NV. Specifically, we propose two algorithms based on fractal models to simulate the main structure of NV and an adaptive color generation method to assign photorealistic pixel values to the structure. Moreover, we develop an interactive system that provides instant visual feedback to support NV simulation guided by an ophthalmologist. This enables us to combine the low level algorithms with high-level human feedback to simulate realistic lesions. Experiments suggest that our method is able to produce simulated NVs that are indistinguishable from real lesions.


Diabetic Retinopathy Enlargement Scale Normal Vessel Early Treatment Diabetic Retinopathy Study Corneal Neovascularization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Javitt, J.C., Aiello, L.P., Chiang, Y., Ferris, F.L., Canner, J.K.S.: Greenfield: Preventive eye care in people with diabetes is cost saving to the federal government. Diabetes Care 17(8), 909–917 (1994)CrossRefGoogle Scholar
  2. 2.
    ETDRS Research Group: Early photocoagulation for diabetic retinopathy: Early treatment diabetic retinopathy study report number 9. Ophthalmology, 766–785 (1998)Google Scholar
  3. 3.
    Lee, S., et al.: Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer and human experts. Arch Ophthalmol 119, 509–515 (2001)Google Scholar
  4. 4.
    Usher, D., et al.: Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med. 21(1), 84–90 (2004)CrossRefGoogle Scholar
  5. 5.
    Hipwell, J.H., et al.: Automated detection of microaneurysms in digital red-free photographs: a diabetic retinopathy screening tool. Diabet Med. 17(8), 588–594 (2000)CrossRefGoogle Scholar
  6. 6.
    Sinthanayothin, C., et al.: Automated detection of diabetic retinopathy on digital fundus images. Diabet. Med. 19(2), 105–112 (2002)CrossRefGoogle Scholar
  7. 7.
    Teng, T., et al.: Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy. Medical & Biological Engineering & Computing 40(1), 2–13 (2002)CrossRefGoogle Scholar
  8. 8.
    Early Treatment Diabetic Retinopathy Study Research Group: Grading diabetic retinopathy from stereoscopic color fundus photographs: An extension of the modified Airlie House classification. ETDRS Report Number 10.  Ophthalmology 98, 786–806 (1991)Google Scholar
  9. 9.
    Landini, G., Misson, G.: Simulation of corneal neovascularization by inverted diffusion limited aggregation. Invest Ophthalmol Vis Sci. 34(5), 1872–1875 (1993)Google Scholar
  10. 10.
    Hoe, C.L., Samei, E., Frush, D.P., Delong, D.M.: Simulation of liver lesions for pediatric CT. Radiology 238(2), 699–705 (2006)CrossRefGoogle Scholar
  11. 11.
    Cross, S.S.: Fractal in pathology. Journal of Pathology 182, 1–8 (1997)CrossRefGoogle Scholar
  12. 12.
    Mandelbrot, B.: The Fractal Geometry of Nature., p. 460. WHFreeman, San Francisco (1982)Google Scholar
  13. 13.
    Masters, B.R.: Fractal analysis of the vascular tree in the human retina. Annu. Rev. Biomed. Eng. 6, 427–452 (2004)CrossRefGoogle Scholar
  14. 14.
    Vicsek, T.: Fractal Growth Phenomena, 2nd edn., pp. 105–111, 111–114 . World Scientific Pub Co Inc, Singapore (1992)zbMATHGoogle Scholar
  15. 15.
    Can, A., Stewart, C.V., Roysam, B., Tanenbaum, H.L.: A Feature-based, robust, hierarchical algorithm for registering pairs of images of the curved human retina. IEEE Transactions on PAMI. 24, 347–364 (2002)Google Scholar
  16. 16.
    Lalibert, F., Gagnon, L., Sheng, Y.: Registration and Fusion of Retinal Images-An Evaluation Study. IEEE Transactions on Medical Imaging 22, 661–673 (2003)CrossRefGoogle Scholar
  17. 17.
    Patton, N., Aslam, T.M., et al.: Retinal image analysis: concepts, applications and potential. Progress in Retinal and Eye Research 25(1), 99–127 (2006)CrossRefGoogle Scholar
  18. 18.
    Stanley, H.E., Amaral, L.A.N., Buldyrev, S.V., Goldberger, A.L., Havlin, S., et al.: Scaling and universality in living systems. Fractals 4, 427–451 (1996)CrossRefGoogle Scholar
  19. 19.
    Masters, B., Platt, D.: Development of human retinal vessels: a fractal analysis. Invest. Ophthalmol. Vis. Sci. 30(Suppl.), 391 (1989)Google Scholar
  20. 20.
    Family, F., Masters, B., Platt, D.: Fractal pattern formation in human retinal vessels. Physica D 38, 98–103 (1989)CrossRefGoogle Scholar
  21. 21.
    Vicsek, T.: Fractal Growth Phenomena, 2nd edn., pp. 119–135. World Scientific Pub Co Inc, Singapore (1992)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xinyu Xu
    • 1
  • Baoxin Li
    • 1
  • Jose F. Florez
    • 2
    • 3
    • 4
  • Helen K. Li
    • 2
    • 3
    • 4
  1. 1.Dept. of Computer Science and EngineeringArizona State UniversityTempeU.S.A
  2. 2.Dept. of Ophthalmology and Visual SciencesThe University of Texas Medical BranchGalvestonU.S.A
  3. 3.School of Health Information SciencesUniversity of Texas Health Science CenterHoustonU.S.A
  4. 4.Universidad De AntioquiaMedellin, ColombiaU.S.A

Personalised recommendations