Skip to main content
SpringerLink
Log in

Simulation of Diabetic Retinopathy Neovascularization in Color Digital Fundus Images

  • Conference paper
Advances in Visual Computing (ISVC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4291))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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)

    Article  Google Scholar 

  2. ETDRS Research Group: Early photocoagulation for diabetic retinopathy: Early treatment diabetic retinopathy study report number 9. Ophthalmology, 766–785 (1998)

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  6. Sinthanayothin, C., et al.: Automated detection of diabetic retinopathy on digital fundus images. Diabet. Med. 19(2), 105–112 (2002)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. Hoe, C.L., Samei, E., Frush, D.P., Delong, D.M.: Simulation of liver lesions for pediatric CT. Radiology 238(2), 699–705 (2006)

    Article  Google Scholar 

  11. Cross, S.S.: Fractal in pathology. Journal of Pathology 182, 1–8 (1997)

    Article  Google Scholar 

  12. Mandelbrot, B.: The Fractal Geometry of Nature., p. 460. WHFreeman, San Francisco (1982)

    Google Scholar 

  13. Masters, B.R.: Fractal analysis of the vascular tree in the human retina. Annu. Rev. Biomed. Eng. 6, 427–452 (2004)

    Article  Google Scholar 

  14. Vicsek, T.: Fractal Growth Phenomena, 2nd edn., pp. 105–111, 111–114 . World Scientific Pub Co Inc, Singapore (1992)

    MATH  Google Scholar 

  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. Lalibert, F., Gagnon, L., Sheng, Y.: Registration and Fusion of Retinal Images-An Evaluation Study. IEEE Transactions on Medical Imaging 22, 661–673 (2003)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  19. Masters, B., Platt, D.: Development of human retinal vessels: a fractal analysis. Invest. Ophthalmol. Vis. Sci. 30(Suppl.), 391 (1989)

    Google Scholar 

  20. Family, F., Masters, B., Platt, D.: Fractal pattern formation in human retinal vessels. Physica D 38, 98–103 (1989)

    Article  Google Scholar 

  21. Vicsek, T.: Fractal Growth Phenomena, 2nd edn., pp. 119–135. World Scientific Pub Co Inc, Singapore (1992)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dept. of Computer Science and Engineering, Arizona State University, Tempe, AZ, U.S.A

    Xinyu Xu & Baoxin Li

  2. Dept. of Ophthalmology and Visual Sciences, The University of Texas Medical Branch, Galveston, TX, U.S.A

    Jose F. Florez & Helen K. Li

  3. School of Health Information Sciences, University of Texas Health Science Center, Houston, TX, U.S.A

    Jose F. Florez & Helen K. Li

  4. Universidad De Antioquia, Medellin, Colombia, U.S.A

    Jose F. Florez & Helen K. Li

Authors
  1. Xinyu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Baoxin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jose F. Florez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Helen K. Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Intel, 95052, Santa Clara, CA, USA

    Ara Nefian

  7. University of California, 430, Computer Science Building, 92697-3425, Irvine, CA, USA

    Gopi Meenakshisundaram

  8. Institute for Data Analysis and Visualization, P.O. Box

    Valerio Pascucci

  9. Department of Computer Science and Engineering, Czech Technical University in Prague, Czech

    Jiri Zara

  10. Rockwell Scientific, 1049 Camino Dos Rios, 91360, Thousand Oaks, CA, USA

    Jose Molineros

  11. Computer Graphics Group, Bielefeld University, D-33501, Bielefeld, Germany

    Holger Theisel

  12. Hewlett Packard Labs, Palo Alto, CA, USA

    Tom Malzbender

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Xu, X., Li, B., Florez, J.F., Li, H.K. (2006). Simulation of Diabetic Retinopathy Neovascularization in Color Digital Fundus Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_43

Download citation

  • DOI: https://doi.org/10.1007/11919476_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48628-2

  • Online ISBN: 978-3-540-48631-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation