- About this Journal ·
- Abstracting and Indexing ·
- Advance Access ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Computational and Mathematical Methods in Medicine
Volume 2012 (2012), Article ID 761901, 11 pages
Diabetic Retinopathy Grading by Digital Curvelet Transform
1Biomedical Engineering Department, Medical Image & Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan 81745319, Iran
2Ophthalmology Department, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
Received 24 May 2012; Accepted 30 July 2012
Academic Editor: Jacek Waniewski
Copyright © 2012 Shirin Hajeb Mohammad Alipour et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- N. Cheung, P. Mitchell, and T. Y. Wong, “Diabetic retinopathy,” The Lancet, vol. 376, no. 9735, pp. 124–136, 2010.
- Q. Mohamed, M. C. Gillies, and T. Y. Wong, “Management of diabetic retinopathy: a systematic review,” Journal of the American Medical Association, vol. 298, no. 8, pp. 902–916, 2007.
- M. Niemeijer, M. D. Abràmoff, and B. Van Ginneken, “Segmentation of the optic disc, macula and vascular arch in fundus photographs,” IEEE Transactions on Medical Imaging, vol. 26, no. 1, pp. 116–127, 2007.
- H. Li and O. Chutatape, “Automated feature extraction in color retinal images by a model based approach,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 2, pp. 246–254, 2004.
- American Academy of Ophthalmology Retina Panel, “Preferred practice pattern guidelines. Diabetic retinopathy,” American Academy of Ophthalmology San Francisco, Calif, USA, http://www.aao.org/ppp.
- M. H. Ahmad Fadzil, H. Nugroho, L. I. Izhar, and H. A. Nugroho, “Analysis of retinal fundus images for grading of diabetic retinopathy severity,” Medical and Biological Engineering and Computing, vol. 49, no. 6, pp. 693–700, 2011.
- H. F. Jelinek, M. J. Cree, J. J. G. Leandro, J. V. B. Soares, R. M. Cesar, and A. Luckie, “Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy,” Journal of the Optical Society of America A, vol. 24, no. 5, pp. 1448–1456, 2007.
- P. Kahai, K. R. Namuduri, and H. Thompson, “A decision support framework for automated screening of diabetic retinopathy,” International Journal of Biomedical Imaging, vol. 2006, Article ID 45806, 8 pages, 2006.
- J. Nayak, P. S. Bhat, R. Acharya U, C. M. Lim, and M. Kagathi, “Automated identification of diabetic retinopathy stages using digital fundus images,” Journal of Medical Systems, vol. 32, no. 2, pp. 107–115, 2008.
- A. Sopharak, B. Uyyanonvara, S. Barman, and T. H. Williamson, “Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods,” Computerized Medical Imaging and Graphics, vol. 32, no. 8, pp. 720–727, 2008.
- T. Walter, J. C. Klein, P. Massin, and A. Erginay, “A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudates in color fundus images of the human retina,” IEEE Transactions on Medical Imaging, vol. 21, no. 10, pp. 1236–1243, 2002.
- W. L. Yun, U. Rajendra Acharya, Y. V. Venkatesh, C. Chee, L. C. Min, and E. Y. K. Ng, “Identification of different stages of diabetic retinopathy using retinal optical images,” Information Sciences, vol. 178, no. 1, pp. 106–121, 2008.
- D. Vallabha, R. Dorairaj, K. Namuduri, and H. Thompson, “Automated detection and classification of vascular abnormalities in diabetic retinopathy,” in Proceedings of 13th IEEE Signals, Systems and Computers, vol. 2, pp. 1625–1629, November 2004.
- R. Priya and P. Aruna, “Review of automated diagnosis of diabetic retinopathy using the support vector machine,” International Journal of Applied Engineering Research, vol. 1, pp. 844–863, 2011.
- S. H. Hajeb, H. Rabbani, and M. R. Akhlaghi, “A new combined method based on curvelet transform and morphological operators for automatic detection of foveal avascular zone,” Signal, Image & Video Processing (Springer). In press.
- E. Candès, L. Demanet, D. Donoho, and L. Ying, “Fast discrete curvelet transforms,” Multiscale Modeling and Simulation, vol. 5, no. 3, pp. 861–899, 2006.
- J. L. Starck, F. Murtagh, E. J. Candès, and D. L. Donoho, “Gray and color image contrast enhancement by the curvelet transform,” IEEE Transactions on Image Processing, vol. 12, no. 6, pp. 706–717, 2003.
- M. Esmaeili, H. Rabbani, A. M. Dehnavi, and A. Dehghani, “Automatic optic disk detection by the use of curvelet transform,” in Proceedings of the 9th International Conference on Information Technology and Applications in Biomedicine, ITAB 2009, pp. 1–4, November 2009.
- M. Esmaeili, H. Rabbani, A. Mehri, and A. Dehghani, “Extraction of retinal blood vessels by curvelet transform,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '09), pp. 3353–3356, November 2009.
- E. D. Pisano, S. Zong, B. M. Hemminger et al., “Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms,” Journal of Digital Imaging, vol. 11, no. 4, pp. 193–200, 1998.
- V. Vapnik, Statistical Learning Theory, Springer, 1998.
- S. H. Hajeb, H. Rabbani, M. R. Akhlaghi, S. H. Haghjoo, and A. R. Mehri, “Analysis of foveal avascular zone for grading of diabetic retinopathy severity 8 based on curvelet transform,” Graefe's Archive for Clinical and Experimental Ophthalmology. In press.
- V. Vapnik, S. Golowich, and A. Smola, “Support vector method for function approximation, regression estimation, and signal processing,” in Advances in Neural Information Processing Systems, M. Mozer, M. Jordan, and T. Petsche, Eds., vol. 9, pp. 281–287, MIT Press, Cambridge, Mass, USA, 1997.
- A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, “Comparative exudate classification using support vector machines and neural networks,” in Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 413–420, September 2002.
- F. Z. Berrichi and M. Benyettou, Automated Diagnosis of Retinal Images Using the Support Vector Machine (SVM), Faculte des Science, Department of Informatique, USTO, Oran, Algerie.
- M. N. Do and M. Vetterli, “The contourlet transform: an efficient directional multiresolution image representation,” IEEE Transactions on Image Processing, vol. 14, no. 12, pp. 2091–2106, 2005.
- I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The dual-tree complex wavelet transform,” IEEE Signal Processing Magazine, vol. 22, no. 6, pp. 123–151, 2005.
- E. P. Simoncelli, W. T. Freeman, E. H. Adelson, and D. J. Heeger, “Shiftable multiscale transforms,” IEEE Transactions on Information Theory, vol. 38, no. 2, pp. 587–607, 1992.
- W. Q. Lim, “The discrete shearlet transform: a new directional transform and compactly supported shearlet frames,” IEEE Transactions on Image Processing, vol. 19, no. 5, pp. 1166–1180, 2010.