An automatic hybrid method for retinal blood vessel extraction

• Autorzy: Yong Yang , Shuying Huang , Nini Rao
• Języki publikacji: EN

Abstrakty

EN

The extraction of blood vessels from retinal images is an important and challenging task in medical analysis and diagnosis. This paper presents a novel hybrid automatic approach for the extraction of retinal image vessels. The method consists in the application of mathematical morphology and a fuzzy clustering algorithm followed by a purification procedure. In mathematical morphology, the retinal image is smoothed and strengthened so that the blood vessels are enhanced and the background information is suppressed. The fuzzy clustering algorithm is then employed to the previous enhanced image for segmentation. After the fuzzy segmentation, a purification procedure is used to reduce the weak edges and noise, and the final results of the blood vessels are consequently achieved. The performance of the proposed method is compared with some existing segmentation methods and hand-labeled segmentations. The approach has been tested on a series of retinal images, and experimental results show that our technique is promising and effective.

Słowa kluczowe

EN

blood vessel extraction; retinal image; mathematical morphology; fuzzy clustering

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2008
• Tom: 18
• Numer: 3
• Strony: 399-407

Daty

wydano

2008

otrzymano

2007-09-19

poprawiono

2008-01-26

poprawiono

2008-04-07

Twórcy

autor

Yong Yang

• School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
• School of Information Management, Jiangxi University of Finance and Economics, Nanchang 330013, P. R. China

autor

Shuying Huang

• School of Electronics, Jiangxi University of Finance and Economics, Nanchang 330013, P. R. China

autor

Nini Rao

• School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China

Bibliografia

• Ayala G., Leon T. and Zapater V. (2005). Different averages of a fuzzy set with an application to vessel segmentation, IEEE Transactions on Fuzzy Systems 13(3): 384-393.
• Bezdek J.C.(1981). Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY.
• Can A., Shen H., Turner J.N., Tanenbaum H.L., and Roysam D. B. (1999). Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Transactions on Information Technology in Biomedicine 3(2): 125-138.
• Chanwimaluang T. and Fan G. (2003). An efficient blood vessel detection algorithm for retinal images using local entropy thresholding, Proceedings of IEEE International Symposium on Circuits and Systems, Bangkok, Thailand, Vol. 5, pp. 21-24.
• 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(3): 263-269.
• Chutatape O., Zheng L. and Krishnan S. M. (1998). Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters, Proceedings of the IEEE Conference on Engineering in Medicine and Biology, Hong Kong, China, Vol. 6, pp. 3144-3149.
• Chutatape O., Zheng L. and Krishnan S. M. (1998). Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters, Proceedings of the IEEE Conference on Engineering in Medicine and Biology, Hong Kong, China, Vol. 6, pp. 3144-3149.
• Cote B., Hart W., Goldbaum M., Kube P. and Nelson M. (1994). Classification of blood vessels in ocular fundus images, Technical report, University of California, San Diego, CA.
• Dunn J.C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters, Journal of Cybernetics 3(3): 32-57.
• EI-Khamy S. E., Ghaleb I. and EI-Yamany N. A. (2002). Fuzzy edge detection with minimum fuzzy entropy criterion, Proceedings of the Mediterranean Electrotechnical Conference, Cairo, Egypt, 1: 498-503.
• Gao X. H., Bharath A., Stanton A., Hughes A., Chapman N. and Thom S. (2001). A method of vessel tracking for vessel diameter measurement on retinal images, Proceedings of IEEE International Conference on Image Processing, Thessaloniki, Greece, Vol. 2, pp. 881-884.
• Hoover A., Kouznetsova V. and Goldbaum M. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response, IEEE Transactions on Medical Imaging, 19(3): 203-210.
• Jorge J. G. L., Joao V. B. S., Roberto M. C. J. and Herbert F. J. (2003). Blood vessels segmentation in non-mydriatic images using wavelets and statistical classifiers, Proceedings of Brazilian Symposium on Computer Graphics and Image Processing, Sao Carlos, Brazil, 1: pp. 262-269.
• Kochner B., Schulmann D., Michaelis M., Mann G. and Englemeier K. H. (1998). Course tracking and contour extraction of retinal vessels from colour fundus photographs: Most efficient use of steerable filters for model based image analysis, SPIE Proceedings of Medical Imaging 3328(2): 755-761.
• Otsu N. (1979). A threshold selection method from gray level histogram, IEEE Transactions on Systems, Man, and Cybernetics 9(1): 62-66.
• Rawi M. A., Qutaishat M. and Arrar M. (2007). An improved matched filter for blood vessel detection of digital retinal images, Computers in Biology and Medicine 37(2): 262-267.
• Riveron E. F. and Guimeras N. G. (2006). Extraction of blood vessels in ophthalmic color images of human retinas, Lecture Notes in Computer Science, 4225: 118-126.
• Serra J. and Soille P. (1994). Mathematical Morphology and Its Applications to Image Processing, Kluwer Academic Publishers, Boston, MA.
• Sinthanayothin C., Boyee J.F., Williamson T.H., Cook H.L., Mensah E., Lal S. and Usher D. (2002). Automatic detection of diabetic retinopathy on digital fundus images, Diabetic Medicine 19(2): 105-112.
• Staal J., Abramoff M.D., Niemeijer M., Viergever M.A. and Ginneken B. V. (2004). Ridge-based vessel segmentation in color images of the retina, IEEE Transactions on Medical Imaging 23(4): 501-509.
• Stapor K., Switonski A., Chrastek R. and Michelson G. (2004). Segmentation of fundus eye images using methods of mathematical morphology for glaucoma diagnosis, Lecture Notes in Computer Science, 3039: 41-48.
• Stapor K. and Switonski A. (2004). Automatic analysis of fundus eye images using mathematical morphology and neural networks for supporting glaucoma diagnosis, Machine Graphics & Vision 13(1/2): 65-78.
• Tamura S., Tanaka K., Ohmori S., Okazaki K., Okada A. and Hoshi M. (1983). Semiautomatic leakage analyzing system for time series fluorescein ocular fundus angiography, Pattern Recognition 16(1): 149-162.
• Zana F. and Klein J.C. (2001). Segmentation of vessellike patterns using mathematical morphology and curvature evaluation, IEEE Transactions on Image Processing 10(7): 1010-1019.

Identyfikatory

DOI

10.2478/v10006-008-0036-5