Automated Combination of Operations for Retinal Blood Vessel Tree Segmentation
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A. Sopharak, B. Uyyanonvara, S. Barman,” Automatic detection of diabetic retinopathy exudate from non-dilated retinal images using mathematical morphology methods,” Doi:10.1016/j.compmedimage, August. 2008.
M Niemeijer,B V Ginneken,D Michael, and Abramoff, “A linking framwork for pixel classification based retinal vessel segmentation,” Biomedical application in mocluer, structure, and function imaging , edited by Xiaoping P. Hu, Anne V. Clough, Proc. Of SPIE Vol. 7262,726216, 2009.
X. Lili, and L. Shugia,” A novel method for blood vessel detection from retinal image,”Xu and Lue; licensee BioBed Center Ltd, 2010.
D Robert, and D G Andrea,”Model based analysis for retinopathy detection”, IEEE EMBs cite international, Lyon, France, Aug 2007.
M. A Usman, T Anam, and A.K Shoab,” Retinal image blood vessel segmentation,” IEEE Xplore, April 2009.
X. Zhang, O. Chutatape,” A SVM Approach for detection of hemorrhage in background diabetic retinopathy,” IEEE No. 0-7803-9048-2, May. 2005.
D. Jayanthi, N.Devi, S. Swarna,” Automatic diagnosis of retinal disease from color retinal image,” International Journal of Computer Science and Information Security, IJCSIS, Vol. 7, No. 1, pp. 234-238, January 2010
N. Patton, T.M. Aslam, T. MacGillivray, I.J. Deary, B. Dhillon, R.H. Eikelboom, K. Yogesan, I.J. Constable,” Retinal image analysis: concept, application and potential,” Elsevier Ltd, 1350-9462. Doi: 10.1016/j.preteyer, July. 2005.
J. Hajer, H. Kamel, E. Noureddine, “ Localization of the optic disk in retinal image using the “watersnake”,” IEEE, 978-1-4244-1692-9, Augest. 2008.
http://www.eri.harvard.edu/faculty/peli/projects/retinal%20image%20processing.html
P. Feng, Y. Pan, B. Wei, W. Jin, “Enhancing Retinal Images by the Contourlet,” Transform. Pattern Recogn. Letters. 28, 516–522, 2007.
N.M. Salem, A.K. Nandi, “ Novel and adaptive contribution of the red channel,” Journal of the Franklin Institute 344, 243-256 (2007).
G. Joshi and J. Sivaswamy, “Computer Vision, Graphics & Image Processing,” 6th indian Conf. on 591-598, 2008.
G. Linda. Shapiro and C. George. Stockman , “Computer Vision”, New Jersey, Prentice-Hall, ISBN 0-13-030796-3 ,pp 279-325, 2001.
A. M. Mendonça and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1200–1213, Sep. 2006.
I. Liu and Y. Sun, “Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme,” IEEE Trans. Med. Imag., vol. 12, no. 2, pp. 334–341, Jun. 1993.
M. Lalonde, L. Gagnon, and M.-C. Boucher. “Non-recursive paired tracking for vessel extraction from retinal images”. In Proc. Of Conference Vision Interface, 2000.
M. Foracchia, E. Grisan, and A. Ruggeri, “ Detection of optic disc in retinal images by means of a geometrical model of vessel structure,” IEEE Trnas. Med. Imag, vol 23, no. 10, pp. 1189-1195, Oct. 2004.
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,” in Medical Data Analysis, ser. Lecture Notes in Computer Science, J. Crespo, V. Maojo, and F. Martin, Eds. Berlin, Germany: Springer-Verlag, pp. 282–287. 2001.
F. Zana and J. C. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Trans. Image Process, vol 10, no 7, pp. 1010-1019, Jul. 2001.
A. M. Mendonça and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1200–1213, Sep. 2006.
S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Trans. Med. Imag., vol. 8, no. 3, pp. 263–269, Sep. 1989.
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, no. 3, pp. 203-210, Mar. 2000.
L. Gang, O. Chutatape, and S. M. Krishnan, “Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter,” IEEE Trans. Biomed. Eng., vol. 49, pp. 168-172, Feb. 2002.
M. Al-Rawi and H. Karajeh, “Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images,” Comput. Methods Program Biomed, vol. 87, pp. 248-253, 2007.
M. Al-Rawi, M. Qutaishat, and M. Arrar, “An improved matched filter for blood vessel detection of digital retinal images,” Comput Biomed , vol. 37, pp. 262-267, 2007.
M. G. Cinsdikici and D. Aydin, “Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm,” Comput. Methods Programs Biomed, vol. 96, pp. 85–95, 2009.
X. Jiang and D. Mojon, “Adaptive local thresholding by verificationbased multithreshold probing with application to vessel detection in retinal images,” IEEE Trans Pattern Anal. Mach. Intell., vol. 25, no. 1, pp. 131–137, Jan. 2003.
R.C. Gonzalez, R.E Woods,” Digital Image Processing,” Pearson Educatio,Inc. 2008,pp 122-827.
G Andres, S Maria and Marruge, Millan, “ Retinal image analysis: Preprocessing and fearure extraction,” doi:10.1088/1742-6596/274/1/02039. journal of physics: conference series 244. 2011.
Digital retinal images for vessel extraction- DRIVE database: http://www.isi.uu.nl/Research/Databases/DRIVE/
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