Abstract
Performing the segmentation of vasculature in the retinal images having pathology is a challenging problem. This paper presents a novel approach for automated segmentation of the vasculature in retinal images. The approach uses the intensity information from red and green channels of the same retinal image to correct non-uniform illumination in color fundus images. Matched filtering is utilized to enhance the contrast of blood vessels against the background. The enhanced blood vessels are then segmented by employing spatially weighted fuzzy c-means clustering based thresholding which can well maintain the spatial structure of the vascular tree segments. The proposed method’s performance is evaluated on publicly available DRIVE and STARE databases of manually labeled images. On the DRIVE and STARE databases, it achieves an area under the receiver operating characteristic curve of 0.9518 and 0.9602 respectively, being superior to those presented by state-of-the-art unsupervised approaches and comparable to those obtained with the supervised methods.
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Kanski, J. J., Clinical Ophthalmology: A systematic approach. Butterworth-Heinemann, London, 1989.
Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M., Detection of blood vessels in retinal images using twodimensional matched filters. IEEE Trans. Med. Imaging 8, 1989 doi: 10.1109/42.34715
Ricci, E., and Perfetti, R., Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification. IEEE Trans. Med. Imaging. 26:1357–1365, 2007. doi:10.1109/TMI.2007.898551
Soares, J. V. B., Leandro, J. J. G., Cesar, R. M. Jr., Jelinek, H. F., and Cree, M. J., Retinal Vessel Segmentation Using the 2-D Gabor Wavelet and Supervised Classification. IEEE Transactions on Medical imaging I. 25:1214–1222, 2006.
Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B., Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging, 501–509 2004. doi: 10.1109/TMI.2004.825627
Sofka, M., and Stewar, C. V., “Retinal vessel extraction using multiscale matched filters confidence and edge measures,” Tech. Rep. 05–20, the Department of Computer Science, Rensselaer Polytechnic Institute, August 16 2005.
Mendonca, A. M., and Campilho, A., Segmentation of retinal blood vessels by combining the detection of centrelines and morphological reconstruction. IEEE Trans. Med. Imaging. 25(9):200–1213, 2006. doi:10.1109/TMI.2006.879955
Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Transactions on Medical imaging, 19:3, 2000. http://www.ces.clemson.edu/ahoover
Jiang, X., Mojon, D., Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans. Pattern Anal. Mach. Intell., 131–137, 2003 doi:10.1109/TPAMI.2003.1159954
Martinez-Perez, M. E., Hughes, A. D., Stanton, A. V., Thom, S. A., Chapman, N., Bharath, A. B., and Parker, K. H., Retinal vascular tree morphology: A semi-automatic quantification. IEEE Trans. Biomed. Eng. 49(8):912–917, 2002. doi:10.1109/TBME.2002.800789.
Zana, F., Klein, J., Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans. Image Process., 1010–1019, 2001 doi:10.1109/83.931095
Fang, B., Hsu, W., Lee, M., “Reconstruction of vascular structures in retinal images,” in Proc. ICIP’03, 2003, pp. II:157–160.
Thitiporn Chanwimaluang and Guoliang Fan, “An Efficient Blood Vessel Detection Algorithm for Retinal Images using Local Entropy Thresholding”, in Proc. of the IEEE International Symposium on Circuits and Systems”, Bangkok, Thailand, May 25–28, 2003.
Salem, N. M., Nandi, A. K., Novel and adaptive contribution of the red channel in pre-processing of colour fundus images. J. Franklin Inst., 2006 doi:10.1016/j.jfranklin.2006.09.001
Li, H., Hsu, W., Lee, M. L., and Wang, H., Automatic grading of retinal vessel calibre. IEEE Trans. Biomed. Eng. 52(7):1352–1355, 2005. doi:10.1109/TBME.2005.847402
Can, A., Shen, H., Turner, J. N., et al., Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Trans. Inf. Technol. Biomed. 3(2):125–138, 1999. doi:10.1109/4233.767088
Tolias, Y. A., and Panas, S. M., A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trans. Med. Imaging. 17(2):263–273, 1998. doi:10.1109/42.700738
Chutatape, O., Zheng, L., and Krishnan, S. M., Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters,. In Proc. of the 20th Annual International Conference of the IEEE Engineering inMedicine and Biology Society, (EMBS'98). 20:3144–3149, 1998.
Gao, X., Bharath, A., et al., “A method of vessel tracking for vessel diameter measurement on retinal images,” In ICIP01,pp.881–884, 2001.
Zhou, L., Rzeszotarski, M. S., Singerman, L. J., and Chokreff, J. M., The detection and quantification of retinopathy using digital angiograms.. IEEE Trans. Med. Imaging. 13(4):619–626, 1994. doi:10.1109/42.363106
Lalonde, M., Gagnon, L., Boucher, M.-C., “Non-recursive paired tracking for vessel extraction from retinal images,” Vision Interface, 61–68, 2000.
Zhang, Y., Hsu, W., and Lee, M. L., Segmentation of Retinal Vessels Using Nonlinear Projections. Proc. ICIP. 5:541–544, 2007.
Niemeijer, M., Staal, J., van Ginneken, B., Loog, M., and Abramoff, M., Comparative study of retinal vessel segmentation methods on a new publicly available database. In J. Michael Fitzpatrick and M. Sonka, editors, SPIE Medical Imaging, 5370:648–656. SPIE, 2004
Yong, Y., Chongxun, Z., Pan, L., “A Novel Fuzzy C-Means Clustering Algorithm for Image Thresholding”, Measurement Science Review, 4, 1, 2004.
Gonzalez, R. C., Woods, R. E., and Eddins, S. L., Digital Image Processing Using Matlab.. Prentice Hall, Pearson, 2004.
Lam, B., and Yan, H., A Novel Vessel Segmentation Algorithm for Pathological Retina Images Based on the Divergence of Vector Fields. IEEE Trans. Med. Imaging. 27:237–246, 2008. doi:10.1109/TMI.2007.909827
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Kande, G.B., Subbaiah, P.V. & Savithri, T.S. Unsupervised Fuzzy Based Vessel Segmentation In Pathological Digital Fundus Images. J Med Syst 34, 849–858 (2010). https://doi.org/10.1007/s10916-009-9299-0
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DOI: https://doi.org/10.1007/s10916-009-9299-0