Subject Areas : journal of Artificial Intelligence in Electrical Engineering
1 -
Keywords:
Abstract :
References:
[1] N. Dey, A. B. Roy, M. Pal, and A. Das, "FCM Based Blood Vessel Segmentation Method for Retinal Images," arXiv preprint arXiv:1209.1181, 2012.
[2] F. M. Villalobos-Castaldi, E. M. Felipe-Riverón, and L. P. Sánchez-Fernández, "A fast, efficient and automated method to extract vessels from fundus images," Journal of Visualization, vol. 13, pp. 263-270, 2010.
[2] E. Ricci and R. Perfetti, "Retinal blood vessel segmentation using line operators and support vector classification," Medical Imaging, IEEE Transactions on, vol. 26, pp. 1357-1365, 2007.
[3] M. G. Cinsdikici and D. Aydın, "Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm," Computer methods and programs in biomedicine, vol. 96, pp. 85-95, 2009.
[4] B. Sumathy and S. Poornachandra, "Retinal blood vessel segmentation using morphological structuring element and entropy thresholding," in Computing Communication & Networking Technologies (ICCCNT), 2012 Third International Conference on, 2012, pp. 1-5.
[5] M. M. Fraz, S. Barman, P. Remagnino, A. Hoppe, A. Basit, B. Uyyanonvara, et al., "An approach to localize the retinal blood vessels using bit planes and centerline detection," Computer methods and programs in biomedicine, vol. 108, pp. 600-616, 2012.
[6] S. Joshi and P. Karule, "Retinal Blood Vessel Segmentation," ed: IJEIT, 2012.
[7] B. Dai, W. Bu, X. Wu, and Y. Teng, "Retinal vessel segmentation via Iterative Geodesic Time Transform," in Pattern Recognition (ICPR), 2012 21st International Conference on, 2012, pp. 561-564.
[8] L. Espona, M. J. Carreira, M. Penedo, and M. Ortega, "Retinal vessel tree segmentation using a deformable contour model," in Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, 2008, pp. 1-4.
[9] A. Asghari Govar and A. Baibourdi Aghdam. " Using modified time-frequency algorithms for retinal vessel detection to diagnose eye diseases, " Journal of Artificial Intelligence in Electrical Engineering,
In press.
[10] M. Roushdy, "Comparative study of edge detection algorithms applying on the grayscale noisy image using morphological filter," GVIP journal, vol. 6, pp. 17-23, 2006.
[11] R. C. Gonzalez and R. E. Woods, "Digital image processing," ed: Prentice hall Upper Saddle River, NJ, 2002.
[12] J. Gasparri, A. Bouchet, G. Abras, V. Ballarin, and J. Pastore, "Medical Image Segmentation using the HSI color space and Fuzzy Mathematical Morphology," in Journal of Physics: Conference Series, 2011, p. 012033.
[13] A. Sopharak, B. Uyyanonvara, and S. Barman, "Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy C-means clustering," Sensors, vol. 9, pp. 2148-2161, 2009.
[14] A. Osareh and B. Shadgar, "Automatic blood vessel segmentation in color images of retina," Iran. J. Sci. Technol. Trans. B: Engineering, vol. 33, pp. 191-206, 2009.
[15] L. Xu and S. Luo, "A novel method for blood vessel detection from retinal images," Biomedical engineering online, vol. 9, p. 14, 2010.
[16] D. Marín, A. Aquino, M. E. Gegúndez-Arias, and J. M. Bravo, "A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features," Medical Imaging, IEEE Transactions on, vol. 30, pp. 146-158, 2011.
[17] S. A. Salem, N. M. Salem, and A. K. Nandi, "Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy," Medical & biological engineering & computing, vol. 45, pp. 261-273, 2007.
[18] Lupascu C.A, Tegolo D, Trucco E, ”FABC: retinal vessel segmentation using AdaBoost”, IEEE Transactions on Information Technology in Biomedicine, vol. 14 ,pp 1267–1274, 2010.
[19] Lam B.S.Y, Yongsheng G, Liew A.W.C, ”General retinal vessel segmentation using regularization-based multiconcavity modeling”, IEEE Transactions on Medical Imaging, vol. 29 ,PP 1369–1381, 2010.
[20] Li W, Bhalerao A, Wilson R, ”Analysis of retinal vasculature using a multiresolution Hermite model”, IEEE Transactions on Medical Imaging. vol. 26, PP 137–152, 2007.
[1] N. Dey, A. B. Roy, M. Pal, and A. Das, "FCM Based Blood Vessel Segmentation Method for Retinal Images," arXiv preprint arXiv:1209.1181, 2012.
[2] F. M. Villalobos-Castaldi, E. M. Felipe-Riverón, and L. P. Sánchez-Fernández, "A fast, efficient and automated method to extract vessels from fundus images," Journal of Visualization, vol. 13, pp. 263-270, 2010.
[2] E. Ricci and R. Perfetti, "Retinal blood vessel segmentation using line operators and support vector classification," Medical Imaging, IEEE Transactions on, vol. 26, pp. 1357-1365, 2007.
[3] M. G. Cinsdikici and D. Aydın, "Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm," Computer methods and programs in biomedicine, vol. 96, pp. 85-95, 2009.
[4] B. Sumathy and S. Poornachandra, "Retinal blood vessel segmentation using morphological structuring element and entropy thresholding," in Computing Communication & Networking Technologies (ICCCNT), 2012 Third International Conference on, 2012, pp. 1-5.
[5] M. M. Fraz, S. Barman, P. Remagnino, A. Hoppe, A. Basit, B. Uyyanonvara, et al., "An approach to localize the retinal blood vessels using bit planes and centerline detection," Computer methods and programs in biomedicine, vol. 108, pp. 600-616, 2012.
[6] S. Joshi and P. Karule, "Retinal Blood Vessel Segmentation," ed: IJEIT, 2012.
[7] B. Dai, W. Bu, X. Wu, and Y. Teng, "Retinal vessel segmentation via Iterative Geodesic Time Transform," in Pattern Recognition (ICPR), 2012 21st International Conference on, 2012, pp. 561-564.
[8] L. Espona, M. J. Carreira, M. Penedo, and M. Ortega, "Retinal vessel tree segmentation using a deformable contour model," in Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, 2008, pp. 1-4.
[9] A. Asghari Govar and A. Baibourdi Aghdam. " Using modified time-frequency algorithms for retinal vessel detection to diagnose eye diseases, " Journal of Artificial Intelligence in Electrical Engineering,
In press.
[10] M. Roushdy, "Comparative study of edge detection algorithms applying on the grayscale noisy image using morphological filter," GVIP journal, vol. 6, pp. 17-23, 2006.
[11] R. C. Gonzalez and R. E. Woods, "Digital image processing," ed: Prentice hall Upper Saddle River, NJ, 2002.
[12] J. Gasparri, A. Bouchet, G. Abras, V. Ballarin, and J. Pastore, "Medical Image Segmentation using the HSI color space and Fuzzy Mathematical Morphology," in Journal of Physics: Conference Series, 2011, p. 012033.
[13] A. Sopharak, B. Uyyanonvara, and S. Barman, "Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy C-means clustering," Sensors, vol. 9, pp. 2148-2161, 2009.
[14] A. Osareh and B. Shadgar, "Automatic blood vessel segmentation in color images of retina," Iran. J. Sci. Technol. Trans. B: Engineering, vol. 33, pp. 191-206, 2009.
[15] L. Xu and S. Luo, "A novel method for blood vessel detection from retinal images," Biomedical engineering online, vol. 9, p. 14, 2010.
[16] D. Marín, A. Aquino, M. E. Gegúndez-Arias, and J. M. Bravo, "A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features," Medical Imaging, IEEE Transactions on, vol. 30, pp. 146-158, 2011.
[17] S. A. Salem, N. M. Salem, and A. K. Nandi, "Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy," Medical & biological engineering & computing, vol. 45, pp. 261-273, 2007.
[18] Lupascu C.A, Tegolo D, Trucco E, ”FABC: retinal vessel segmentation using AdaBoost”, IEEE Transactions on Information Technology in Biomedicine, vol. 14 ,pp 1267–1274, 2010.
[19] Lam B.S.Y, Yongsheng G, Liew A.W.C, ”General retinal vessel segmentation using regularization-based multiconcavity modeling”, IEEE Transactions on Medical Imaging, vol. 29 ,PP 1369–1381, 2010.
[20] Li W, Bhalerao A, Wilson R, ”Analysis of retinal vasculature using a multiresolution Hermite model”, IEEE Transactions on Medical Imaging. vol. 26, PP 137–152, 2007.
