Retinal Vessel Segmentation Using Multi-Scale Line Detection

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Inspection of the retinal vasculature may reveal precursors of serious diseases such as hypertension, diabetes, cardiovascular disease and stroke. In this paper an effective method for automatically extracting the vascular network in retinal images is presented. The proposed method is based on a multi-scale line detection, which is the line responses at varying scales. Linearly combining these line responses produces the final segmentation for each retinal image. The multi-scale line detection is applied on a vessel enhanced image whose noise and optic disc is removed and the contrast of blood vessels (including thin vessels) is enhanced by top-hat transformation and line detector filter. The preprocessing with retinal image results in the response of thin vessels with multi-scale detection is more sensitive and free from the influence of the optic disc, so the proposed method can get the very detail vascular tree in segmentation results. The performance of the proposed method was evaluated on two publicly available DRIVE, STARE databases. Experimental results have also shown the proposed method achieves high local accuracy (a measure to assess the accuracy at regions around the vessels) and approximates the average accuracy of a human observer. Moreover, the method is simple, fast, and robust to noise, so suitable for being integrated into a computer-assisted diagnostic system for ophthalmic disorders.
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Retinal Image; Vessel Segmentation; Line Detector; Top-Hat Transformation

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B.S.Y. Lam, Y. Gao, A.W.C. Liew, General retinal vessel segmentation using regularization-based multiconcavity modelling, IEEE Transactions on Medical Imaging, Vol. 29, n. 7, pp. 1369-1381, 2010.

N. Neda, P. Hossein. Optical Disc and Diabetic Retinopathy Detection in Retinal Images Using Morphological Operation and Region Growing, International Review on Computers and Software, Vol. 7, n. 4, pp. 1463-1469, 2012.

M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. Rudnicka, C. Owen, S. Barman, Blood vessel segmentation methodologies in retinal images—a survey, Computer Methods and Programs in Biomedicine. Computer methods and programs in biomedicine, Io8, pp. 407-433, 2012.

O.R.A.U. Faust, E. Ng, K.H. Ng, J. Suri, Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review, Journal of Medical Systems, Vol. 36, pp. 1-13, 2010.

F. Zana, J.-C. Klein, Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation, IEEE Transactions on Medical Imaging, Vol. 11, no. 7, pp. 1111-1119, 2001.

S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, M. Goldbaum, Detection of blood vessels in retinal images using two-dimensional matched filters, IEEE Transactions on Medical Imaging, Vol.8, n.3, pp. 263-269, 1989.

E. Ricci, R. Perfetti, Retinal blood vessel segmentation using line operators and support vector classification, IEEE Transactions on Medical Imaging, Vol.26, n.10, pp. 1357-1365, 2007.

Andrew Hunter, James Lowell, and David Steel, Tram-line filtering for retinal vessel segmentation, The 3rd European Medical and Biological Engineering Conference EMBEC’05 , Vol. 11, November 2005.

Uyen T.V. Nguyen, A. Bhuiyan, Laurence A.F. Park. An effective retinal blood vessel segmentation method using multi-scale line detection. Pattern Recognition, Vol.46, pp. 703-715, 2013.

Research Section, Digital Retinal Image for Vessel Extraction (DRIVE) Database. Utrecht, The Netherlands, Univ. Med. Center. Utrecht, Image Sci. Inst. [Online]. Available:

STARE ProjectWebsite. Clemson, SC, Clemson Univ. [Online]. Available:

M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. D. Abr àmoff. Comparative study of retinal vessel segmentation methods on a new publicly available database, Proc. SPIE Medical Imaging. M. Fitzpatrick and M. Sonka, Eds., Vol. 5370, pp. 648-656, 2004.

J. Serra, Image Analysis and Mathematical Morphology. London, U.K.: Academic, 1982.

T. Walter and J.-C. Klein, Segmentation of color fundus images of the human retina: Detection of the optic disc and the vascular tree using morphological techniques, Lecture Notes Computer Science. Berlin: Springer -Verlag, Vol. 2199, pp. 282 –287, 2001.

D. Marin, A. Aquino, M.E. Gegundez-Arias, J.M. Bravo. A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features, IEEE Transactions on Medical Imaging, Vol.30, pp. 146-158, 2011.

A. M. Mendonça and A. Campilho, Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction, IEEE Transactions on Medical Imaging,, Vol. 25, no. 9, pp. 1200-1213, 2006.


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