Biologically-Inspired Supervised Vasculature Segmentation in SLO Retinal Fundus Images

  • Samaneh Abbasi-Sureshjani
  • Iris Smit-Ockeloen
  • Jiong Zhang
  • Bart Ter Haar Romeny
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)

Abstract

We propose a novel Brain-Inspired Multi-Scales and Multi-Orientations (BIMSO) segmentation technique for the retinal images taken with laser ophthalmoscope (SLO) imaging cameras. Conventional retinal segmentation methods have been designed mainly for color RGB images and they often fail in segmenting the SLO images because of the presence of noise in these images. We suppress the noise and enhance the blood vessels by lifting the 2D image to a joint space of positions and orientations (SE(2)) using the directional anisotropic wavelets. Then a neural network classifier is trained and tested using several features including the intensity of pixels, filter response to the wavelet and multi-scale left-invariant Gaussian derivatives jet in SE(2). BIMSO is robust against noise, non-uniform luminosity and contrast variability. In addition to preserving the connections, it has higher sensitivity and detects the small vessels better compared to state-of-the-art methods for both RGB and SLO images.

Keywords

Scanning laser ophthalmoscope Primary visual cortex Anisotropic wavelets Multi-scale Orientation score Left-invariant Gaussian derivatives Blood vessel segmentation Diabetic retinopathy 

Notes

Acknowledgements

This project has received funding from the European Union’s Seventh Framework Programme, Marie Curie Actions- Initial Training Network, under grant agreement \(n^\circ 607643\) “Metric Analysis For Emergent Technologies (MAnET)”.

References

  1. 1.
    Azzopardi, G., Strisciuglio, N., Vento, M., Petkov, N.: Trainable cosfire filters for vessel delineation with application to retinal images. Med. Image Anal. 19(1), 46–57 (2015)CrossRefGoogle Scholar
  2. 2.
    Bekkers, E., Duits, R., Berendschot, T., ter Haar Romeny, B.: A multi-orientation analysis approach to retinal vessel tracking. J. Math. Imaging Vis. 49(3), 583–610 (2014)MATHCrossRefGoogle Scholar
  3. 3.
    Duits, R.: Perceptual organization in image analysis. Ph.D. thesis, Eindhoven University of Technology, Department of Biomedical Engineering, The Netherlands (2005)Google Scholar
  4. 4.
    Duits, R., Felsberg, M., Granlund, G., ter Haar Romeny, B., et al.: Image analysis and reconstruction using a wavelet transform constructed from a reducible representation of the euclidean motion group. Int. J. Comput. Vision 72(1), 79–102 (2007)CrossRefGoogle Scholar
  5. 5.
    Foracchia, M., Grisan, E., Ruggeri, A.: Luminosity and contrast normalization in retinal images. Med. Image Anal. 9(3), 179–190 (2005)CrossRefGoogle Scholar
  6. 6.
    Franken, E.M.: Enhancement of crossing elongated structures in images. Ph.D. thesis, Eindhoven University of Technology. Eindhoven, The Netherlands (2008)Google Scholar
  7. 7.
    Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106 (1962)CrossRefGoogle Scholar
  8. 8.
    Kanski, J.J., Bowling, B.: Synopsis of Clinical Ophthalmology. Elsevier Health Sciences, Amsterdam (2012)Google Scholar
  9. 9.
    Krause, M., Alles, R.M., Burgeth, B., Weickert, J.: Fast retinal vessel analysis. J. Real-Time Image Proc. 1–10 (2013). http://link.springer.com/article/10.1007%2Fs11554-013-0342-5
  10. 10.
    Lindeberg, T.: Scale-space Theory in Computer Vision. Springer Science & Business Media, New York (1993)MATHGoogle Scholar
  11. 11.
    Marin, D., Aquino, A., Gegundez-Arias, M., Bravo, J.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146–158 (2011)CrossRefGoogle Scholar
  12. 12.
    Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., Abramoff, M.D.: Comparative study of retinal vessel segmentation methods on a new publicly available database. In: Medical Imaging 2004, pp. 648–656. International Society for Optics and Photonics (2004)Google Scholar
  13. 13.
    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 Trans. Med. Imaging 25(9), 1214–1222 (2006)CrossRefGoogle Scholar
  14. 14.
    Staal, J., Abràmoff, M.D., Niemeijer, M., Viergever, M.A., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004)CrossRefGoogle Scholar
  15. 15.
    Viswanath, K., McGavin, D.M.: Diabetic retinopathy: clinical findings and management. Community Eye Health 16(46), 21 (2003)Google Scholar
  16. 16.
    Xu, J., Ishikawa, H., Wollstein, G., Schuman, J.S.: Retinal vessel segmentation on slo image. In: 30th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, 2008, EMBS 2008, pp. 2258–2261. IEEE (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Samaneh Abbasi-Sureshjani
    • 1
  • Iris Smit-Ockeloen
    • 1
  • Jiong Zhang
    • 1
  • Bart Ter Haar Romeny
    • 1
    • 2
  1. 1.Eindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Biomedical and Information EngineeringNortheastern UniversityShenyangChina

Personalised recommendations