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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Provided by i-Optics B.V. company in the Netherlands.
References
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)
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)
Duits, R.: Perceptual organization in image analysis. Ph.D. thesis, Eindhoven University of Technology, Department of Biomedical Engineering, The Netherlands (2005)
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)
Foracchia, M., Grisan, E., Ruggeri, A.: Luminosity and contrast normalization in retinal images. Med. Image Anal. 9(3), 179–190 (2005)
Franken, E.M.: Enhancement of crossing elongated structures in images. Ph.D. thesis, Eindhoven University of Technology. Eindhoven, The Netherlands (2008)
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)
Kanski, J.J., Bowling, B.: Synopsis of Clinical Ophthalmology. Elsevier Health Sciences, Amsterdam (2012)
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
Lindeberg, T.: Scale-space Theory in Computer Vision. Springer Science & Business Media, New York (1993)
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)
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)
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)
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)
Viswanath, K., McGavin, D.M.: Diabetic retinopathy: clinical findings and management. Community Eye Health 16(46), 21 (2003)
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)
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)”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Abbasi-Sureshjani, S., Smit-Ockeloen, I., Zhang, J., Ter Haar Romeny, B. (2015). Biologically-Inspired Supervised Vasculature Segmentation in SLO Retinal Fundus Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-20801-5_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20800-8
Online ISBN: 978-3-319-20801-5
eBook Packages: Computer ScienceComputer Science (R0)