Retinal Vessel Extraction and Vessel Path Prediction by Active Contouring
the author of the article can submit here a request for assignment of a DOI number to this resource!
Cost of the service: euros 10,00 (for a DOI)
Diabetic retinopathy affects eye sight by damaging the small blood vessels present at the retina, vessels becomes narrow and micro aneurysms are formed inside the retina. In this proposed work, an efficient approach of filtering and thresholding is presented for the accurate extraction of retinal blood vessel. A systematic approach is used here for vessel extraction with less complex algorithm. Gaussian high pass filter is used for edge detection and global thresholding is done for contrast enhancement. To make the vessel structure continuous, Gabor filter is used. For tracing the lost nerve path, Active contouring is performed on threshold image, which is an energy minimization process. An approach of convolution of Kirsch template with retinal image is used with high directional selectivity for the minute edges. This present system performs well for the extraction of vessels in retinal image
Copyright © 2014 Praise Worthy Prize - All rights reserved.
Bob Zhang, Lin Zhang, Lei Zhang and Fakhri Karray, “Retinal Vessel Extraction by Matched Filter with First-Order Derivative of Gaussian” Dept. of Electrical and Computer Engineering, University of Waterloo, ON, Canada, N2L 3G1.
Y. A. Alsultanny, Region Growing and Segmentation Based on by 2D Wavelet Transform to the Color Images, (2008) International Review on Computers and Software (IRECOS), 3. (3), pp. 315 - 323.
Jaspreet kaur and Dr. H.P Sinha “Automated detection of Retinal Blood vessels in Diabetic retinopathy using Gabor filter” in IJCSNS international journal of computer science and network security, vol.12 No.12, April 2012.
F. Bouchareb, R. Hamdi, New Methods of Image Analysis Applied to Hand-Written Arabic Character Recognition, (2008) International Review on Computers and Software (IRECOS), 3. (5), pp. 447 - 450.
J. G.O´Shea and D. A. Infeld, “Screening and monitoring diabetic retinopathy,” Birmingham and Midland Eye Centre, 1999.
Gao, X., Retinal vessel segmentation using multi-scale line detection, (2013) International Review on Computers and Software (IRECOS), 8 (2), pp. 613-619.
S. Slatnia, O. Kazar, Generalised Evolutionary Cellular Automata Based-approach for Edge Detection, (2008) International Review on Computers and Software (IRECOS), 3. (4), pp. 424 - 428..
D Huang, EA Swanson, CP Lin, JS Schuman, WG Stinson, et.al Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge 02139.
M. Niemeijer, J.J. Staal, B. van Ginneken, M. Loog, and M.D. Abramoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database”, SPIE MedicalImaging, vol. 5370, pp. 648-656, 2004
S.Garg, J. Sivaswamy, and S. Chandra, “Unsupervised curvature-based retinal vessel” segmentation,” Proc. of IEEE International Symposium on Bio-Medical Imaging, pp. 344 – 347, 2007.
A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Trans. Med. Imag., vol. 19, pp. 203–210, Mar. 2000.
J.J. Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, and B. van Ginneken, “Ridge based vessel segmentation in color images of the retina,” IEEE Transactions on Medical Imaging, pp. 501–509, 2004.
F. Zana, and J.C. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Process., pp. 1010–1019, 2001.
C. Sinthanayothin, J. F. Boyce, T. H. Williamson, H. L. Cook, E. Mensah, S. Lal, and D. Usher. Automated detection of diabetic retinopathy on digital fundus images. Diabetic Medicine, 19:105–112, 2002.
Mark Nixon and Alberto Aguado “Feature extraction and image processing” 2nd vol.
Leymarie, F. ; Levine, M.D “Tracking deformable objects in the plane using an active contour model”, IEEE trans. Pattern analysis and machine intelligence, vol. 15, pp. 617 – 634.
Rafael C. Gonzalez and Richard E.Woods “Digital image processing” 3rd vol.
Rahil Garnavi, Mohammad Aldeen, Sue FinchandGeorge Varigos, “Global versus hybrid thresholding for border detection in dermoscopy images” Lecture Notes in Computer Science, Volume 6134, 2010, pp 531-540.
Lee, S. A., Chung, S. Y. and Park, R. H., A Comparative Performance Study of Several Global thresholding Techniques for Segmentation, CVGIP, 52, pp. 171–190, 1990.
Sahoo, P. K., Soltani, S., Wong, A. K. C. and Chen, Y. C., Survey of Thresholding Techniques, CVGIP, 41(2), pp. 233–260, 1988.
Wai Kin Kong, David Zhang, Wenxin Li “Palmprint feature extraction using 2-D gabor filters” Elsevier , Pattern Recognition 36 (2003) 2339 – 2347, 2002.
DaDavatzikos, C.A; Prince, J.L “An active contour model for mapping the cortex” IEEE transactions, Medical imaging vol: 14 Issue: 1, 0278-0062, mar 1995
Michael kass, Andrew witkin and Demetri terzopoulos, “Snakes: Active contour models “International journal of computer vision, 321-331, 1988
H. B. Kekre, Saylee M. Gharge “Image segmentation using extended edge operator for mammographic images” IJCSE, Vol. 02, No. 04, 2010, 1086-1091.
Du, X., Dang, J., Wang, Y., An image segmentation algorithm of snake model based on firefly algorithm, (2012) International Review on Computers and Software (IRECOS), 7 (5), pp. 2718-2723.
- There are currently no refbacks.
Please send any question about this web site to email@example.com
Copyright © 2005-2019 Praise Worthy Prize