Advertisement

Journal of Real-Time Image Processing

, Volume 11, Issue 2, pp 413–422 | Cite as

Fast retinal vessel analysis

  • Michael KrauseEmail author
  • Ralph Maria Alles
  • Bernhard Burgeth
  • Joachim Weickert
Special Issue Paper

Abstract

We introduce a fast image processing system that allows to analyse digital data-bases of retinal images in a short time, and to process the image in situ while the patient is examined. While it achieves a comparable quality as state-of-the-art methods, it differs from most of them by the fact that it is extremely fast. Retinal blood vessels are enhanced via convolution with the second derivative of the local Radon kernel. It is rotated by different angles, and it adapts itself via a maximisation procedure to the vessel directions. We combine smoothing along vessel directions with contrast enhancement across them. We detect vessels as connected structures with very few interruptions. A subsequent skeletonisation allows a higher-level description of the vessel tree. To end up with a very fast system, we combine efficient algorithms for numerical integration, differentiation and interpolation, and we propose an automatic parameter selection strategy. Our convolution kernels are precomputed and stored into cached constant memory. All essential subroutines are intrinsically parallel, and the resulting system is implemented on GPUs using CUDA. Our qualitative evaluations with the DRIVE database and our own database show that the system achieves competitive performance. It is possible to process images of size 4, 288 × 2, 848 pixels in 1.2 s on an NVIDIA Geforce GTX680. Compared to our sequential implementation, this amounts to a speed-up by two orders of magnitude.

Keywords

NVIDIA CUDA Real-time retinal imaging Vessel analysis Vessel segmentation 

Mathematics Subject Classfication

65Y05 65D99 

Notes

Acknowledgements

We thank the authors of the DRIVE database for making their database available and thus allowing us to evaluate our results. Furthermore, we like to thank Rüdiger Leilich for providing the tool ”Algo-Verifier” for better visualisation and documentation of the results in our specifically designed database.

References

  1. 1.
    Chanwimaluang T., Fan G. (2003) An efficient algorithm for extraction of anatomical structures in retinal images. Proc. IEEE Int. Conf. Image Process. 1: 1093–1096Google Scholar
  2. 2.
    Chapman N., Witt N., Bharat A., et al (2001) Computer algorithms for the automated measurement of retinal arteriolar diameters. British J Ophtalmol 85:74–79CrossRefGoogle Scholar
  3. 3.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. MIT Press, Cambridge, MA (1990)Google Scholar
  4. 4.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Medical Image Computing and Computer-Assisted Intervention- MICCAI’98, Lecture Notes in Computer Science, vol. 1496, Springer, pp. 130–137 (1998)Google Scholar
  5. 5.
    Gang L., Chutape O., Krishnan M. (2002) Detection and measurement of vessels in fundus images using amplitude modified second-order Gaussian filter. IEEE Trans. Biomed. Eng. 49(2):168–172CrossRefGoogle Scholar
  6. 6.
    Gao X., Bharat A., Hughes A., et al.: Towards retinal vessel parameterization. In: Medical Imaging 1997: Image Processing, SPIE Proc. vol. 3034, pp. 734–744 (1997)Google Scholar
  7. 7.
    Gonzalez, R., Woods, R.: Digital Image Processing. Prentice Hall, New Jersey (2002)Google Scholar
  8. 8.
    Hoover A., Kouznetsova V., Goldbaum M. (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3):203–210CrossRefGoogle Scholar
  9. 9.
    Hwu, W.M.W.; GPU Computing Gems Emerald Edition. Morgan Kaufmann, Los Altos, CA (2011)Google Scholar
  10. 10.
    Kirk, D.B.; Hwu, W.M.W.: Programming Massively Parallel Processors: A Hands-on Approach. Morgan Kaufmann, Los Altos, CA (2010)Google Scholar
  11. 11.
    Krause, M.: Corner detection in digital images using local tomography. Bachelor’s thesis, Department of Mathematics and Computer Science, Saarland University (2006)Google Scholar
  12. 12.
    Lowell J., Hunter A., Steel D., Basu A., Kennedy R.L. (2004) Measurement of retinal vessel widths from fundus images based on 2-D modeling. IEEE Trans. Med. Imaging 23(10):1196–1204CrossRefGoogle Scholar
  13. 13.
    Mendonça AM., Campilho A. (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reonstruction. IEEE Trans. Med. Imaging 25(9):1200–1213CrossRefGoogle Scholar
  14. 14.
    Niemeijer, M., van Ginneken, B.: Drive database. www.isi.uu.nl/Research/Databases/DRIVE/results.php(2002)
  15. 15.
    Niemeijer M., Staal J., van Ginneken B., Loog M., Abrámoff M.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Fitzpatrick M., Sonka M. (eds.) Proceedings ofSPIE Medical Imaging,vol. 5370, pp. 648–656 (2004)Google Scholar
  16. 16.
    NVIDIA.: Nvidia. http://www.nvidia.com (2012)
  17. 17.
    Ricci E., Perfetti R. (2007) Retinal blood vessel segmentation using line operators and support vector classification. IEEE Trans. Med. Imaging 26(10):1357–1365CrossRefGoogle Scholar
  18. 18.
    Sanders, J., Kandrot, E.: (2011) CUDA by Example, An Introduction to General-Purpose GPU programming. Addison Wesley, Reading, MAGoogle Scholar
  19. 19.
    Savarimuthu T.R., Kjaer-Nielsen A., Sorensen A.S. (2011) Real-time medical video processing, enabled by hardware accelerated correlations. J Real-Time Image Process. 6:187–197CrossRefGoogle Scholar
  20. 20.
    Soares JVB., Leandro JJG., Cesar RM Jr., Jelinek HF., Cree MJ. (2006) Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9):1214–1222CrossRefGoogle Scholar
  21. 21.
    Sofka M., Stewart C. (2006) Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures. IEEE Trans. Med. Imaging 25(12):1531–1546CrossRefGoogle Scholar
  22. 22.
    Soille, P.: Morphological Image Analysis. Springer (1999)Google Scholar
  23. 23.
    Staal J., Abrámoff M.D., Viergever M.A., van Ginneken B. (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4):501–509CrossRefGoogle Scholar
  24. 24.
    van Ginkel, M.: Image analysis using orientation space based on steerable filters. PhD thesis, Delft University of Technology. http://www.ph.tn.tudelft.nl/michael/publications.html (2002)
  25. 25.
    Vermeer K., Vos F., Lemij H., Vossepoel A. (2004) A model based method for retinal blood vessel detection. Comput. Biol. Med. 34:209–219CrossRefGoogle Scholar
  26. 26.
    Wang L., Bhalerao A., Wilson R. (2007) Analysis of retinal vasculature using a multiresolution Hermite model. IEEE Trans. Med. Imaging 26(2):137–152CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael Krause
    • 1
    Email author
  • Ralph Maria Alles
    • 2
  • Bernhard Burgeth
    • 3
  • Joachim Weickert
    • 4
  1. 1.Institute of MicroelectronicsSaarland UniversitySaarbrückenGermany
  2. 2.Augenärzte Bies-Alles-Mély-HadaviSaarlouisGermany
  3. 3.Department of Mathematics, Faculty of Mathematics and Computer ScienceSaarland UniversitySaarbrückenGermany
  4. 4.Mathematical Image Analysis Group, Faculty of Mathematics and Computer ScienceSaarland UniversitySaarbrückenGermany

Personalised recommendations