Abstract
This paper represents an algorithm based on curvature evaluation and Entropy Filtering techniques with texture mapping for guideline. The method is used for the detection of blood vessels from colour retinal fundus images. In order to evaluate vessel-like patterns, segmentation is performed with respect to a precise model. We evaluate the curvature of blood vessels via carrying out eigenvalue analysis of Hessian matrix. This method allows to extract the fine retinal image ridge but introduces the effect of central light reflexes. We apply entropy filtering techniques to calculate the segmentations in relation to central reflex vessels. For efficient differentiation of vessels from analogous background patterns, we use spectral clustering to partition the image texture. It is an alternative of traditional intensity thresholding operation and allows more automatic processing of retinal vessel images. The detection algorithm that derives directly from this modeling is based on five steps: 1) image preprocessing; 2) curvature evaluation; 3) entropy filtering; 4) texture mapping; 5) morphology operation with application of vessel connectivity constraints.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 23(4), 501–509 (2004)
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), 203–210 (2000)
Miri, M., Mahloojifar, A.: Retinal image analysis using curvelet transform and multistructure elements morphology by reconstruction. IEEE Transactions on Medical Imaging 58(5), 1183–1192 (2011)
Zana, F., Klein, J.-C.: Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Transactions on Image Processing 10(7), 1010–1019 (2001)
O’Callaghan, R., Bull, D.: Combined morphological-spectral unsupervised image segmentation. IEEE Transactions on Medical Imaging 14(1), 49–62 (2005)
Yin, X.X., Ng, B.W.-H., Yang, Q., Pitman, A., Ramamohanarao, K., Abbott, D.: Anatomical landmark localization in breast dynamic contrast-enhanced MR imaging. Medical & Biological Engineering & Computing 50(1), 91–101 (2012)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Chanwimaluang, T., Fan, G.: An efficient blood vessel detection algorithm for retinal images using local entropy thresholding. In: Proceedings of the 2003 International Symposium on Circuits and Systems, ISCAS 2003, vol. 5, pp. V21–V24 (2003)
Patton, N., Aslam, T.M., MacGillivray, T., Deary, I.J., Dhillon, B., Eikelboom, R.H., Yogesan, K., Constable, I.J.: Retinal image analysis: concepts, applications and potential. Progress in Retinal and Eye Research 25(1), 99–127 (2006)
Mendonça, A.M., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Transactions on Medical Imaging 25(9), 1200–1213 (2006)
Bankhead, P., Scholfield, C.N., McGeown, J.G., Curtis, T.M.: Fast retinal vessel detection and measurement using wavelets and edge location refinement. PLoS ONE 7(3), art. no. e32435 (2012)
Louisa, L., Lee, S.-W., Suen, C.: Thinning methodologies—a comprehensive survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(9), 869–885 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Yin, XX., Ng, B.W.H., He, J., Zhang, Y., Abbott, D. (2014). Unsupervised Segmentation of Blood Vessels from Colour Retinal Fundus Images. In: Zhang, Y., Yao, G., He, J., Wang, L., Smalheiser, N.R., Yin, X. (eds) Health Information Science. HIS 2014. Lecture Notes in Computer Science, vol 8423. Springer, Cham. https://doi.org/10.1007/978-3-319-06269-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-06269-3_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06268-6
Online ISBN: 978-3-319-06269-3
eBook Packages: Computer ScienceComputer Science (R0)