Automatic detection of multiple oriented blood vessels in retinal images

DOI: 10.4236/jbise.2010.31015   PDF   HTML     7,721 Downloads   15,168 Views   Citations


Automatic segmentation of the vasculature in retinal images is important in the detection of diabetic retinopathy that affects the morphology of the blood vessel tree. In this paper, a hybrid method for efficient segmentation of multiple oriented blood vessels in colour retinal images is proposed. Initially, the appearance of the blood vessels are enhanced and background noise is suppressed with the set of real component of a complex Gabor filters. Then the vessel pixels are detected in the vessel enhanced image using entropic thresholding based on gray level co-occurrence matrix as it takes into account the spatial distribution of gray levels and preserving the spatial structures. The performance of the method is illustrated on two sets of retinal images from publicly available DRIVE (Digital Retinal Images for Vessel Extraction) and Hoover’s databases. For DRIVE database, the blood vessels are detected with sensitivity of 86.47±3.6 (Mean±SD) and specificity of 96±1.01.

Share and Cite:

Siddalingaswamy, P. and Prabhu, K. (2010) Automatic detection of multiple oriented blood vessels in retinal images. Journal of Biomedical Science and Engineering, 3, 101-107. doi: 10.4236/jbise.2010.31015.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Emily, Y.C. (2003) Diabetic retinopathy. Preferred Practice Patterns, American Academy of Ophthalmology, USA.
[2] Thomas, W., Klein, J.C., Pascale, M. and Ali, E. (2002) A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans. Medical. Imaging, 21, 1236-1243.
[3] Laliberté, F., Gagnon, L. and Sheng, Y. (2003) Registration and fusion of retinal images: an evaluation study. IEEE Trans. Medical. Imaging, 22, 661-673.
[4] Hoover, A., Kouznetsoza, V., Goldbaum, M. (2000) Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, 19, 203-210.
[5] Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M. and Goldbaum, M. (1989) Detection of blood vessels in retinal images using two dimensional matched filters. IEEE transactions on Medical Imaging, 8, 263-269.
[6] Thitiporn, C. and Fan, G.L. (2003) An efficient Algorithm for extraction of anatomical structures in retinal images. Proc. Of Intl. Conf. on Image Processing, 1, 1093-1096.
[7] Wu, D., Zhang, M., Liu, J.C. and Wendall, B. (2006) On the adaptive detection of blood vessels in retinal images. IEEE Transactions on Biomedical Engineering, 53, 341-343.
[8] Pinz, A., Bernogger, S., Datlinger, P. and Kruger, A. (1998) Mapping the human retina. IEEE Transactions on Medical imaging, 17, 606-619.
[9] Sinthanayothin, C., Boyce, J.F., Cook, H.L. and Williamson H.T. (1999) Automated location of the optic disc, fovea, and retinal blood vessels from digital color fundus images. British Journal of Ophthalmology, 83, 902-910.
[10] Zana, F. and Klein, J. (2001) Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Transactions on Image Processing, 10, 1010-1019.
[11] Siddalingaswamy, P.C., Prabhu, G. K. and Mithun, D. (2006) Feature extraction of retinal image. Proc. Of the National Conference for PG and Research scholars, 24-27.
[12] Yang, Y., Huang, S.Y. and Rao, N. (2008) An automatic hybrid method for retinal blood vessel extraction. Journal of Appl. Math. Comp. Sci., 18, 399-407.
[13] Lee, T.S. (1996) Image representation using 2D Gabor wavelets. IEEE Transactions of Pattern Analysis and Machine Intelligence. 18, 959-971.
[14] Chen, J., Sato, Y. and Tamura, S. (2000) Orientation space filtering for multiple orientation line segmentation. IEEE Transactions of Pattern Analysis and Machine Intelligence, 22, 417-429.
[15] Liu, Z.Q., Cai, J. and Buse, R. (2003) Hand-writing recognition: soft computing and probablistic approaches. Springer Verlag. Berlin.
[16] Yang, C.W., Chung, P.C. and Chang, C.I. (1996) Hierarchical fast two dimensional entropic thresholding algorithm using a histogram pyramid. Optical Engineering, 35, 3227-3241.
[17] Mark, L. Althouse, G. and Chang, C. (1995) Target detection in multispectral images using the spectral co- occurrence matrix and entropy thresholding. Optical Engineering, 34, 2135-2148.
[18] Mokji, M.M. and S.A.R and Bakar A. (2007) Adaptive thresholding based on co occurrence matrix edge information. Journal of Computers, 2, 44-52.
[19] Staal, J.J., Abràmoff, M.D., Niemeijer, M., Viergever, M. A. and van Ginneken, B. (2004) Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imag., 23, 501-509.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.