References
- M. D. Abramoff, J. M. Reinhardt, S. R. Russell, J. C. Fork, V. B. Mahajan, N. Niemeijer, and G. Quellec, “Automated early detection of diabetic retinopathy,” Ophthalmology, Vol. 117, pp. 1147–54, Jun. 2010.
- N. S. Datta, P. Saha, H. S. Dutta, D. Sarkar, S. Biswas, and P. Sarkar, “A new contrast enhancement method of retinal images in diabetic screening system,” in Proceedings of International Conference on Recent Trends in Information Systems, Kolkata, 2015, pp. 255–60.
- A. Sopharak, B. Uyyanonvara, and S. Barman, “Fine microaneurysms detection from non-dilated diabetic retinopathy retinal images using a hybrid approach,” in Proceedings of The World Congress on Engineering, Vol. 2, London, 2012, pp. 1207–10.
- H. Poostchi, S. Khakmardan, and H. Pourreza, “Diabetic retinopathy dark lesion detection: preprocessing phase,” in Proceedings of Computer and Knowledge Engineering, Iran, 2011, pp. 177–82.
- B. S. Min, D. K. Lim, S. J. Kim, and J. H. Lee, “A novel method of determining parameters of clahe based on image entropy,” Int. J. Software Eng. Appl., Vol. 7, Jul. 2013, pp. 113–20.
- H. Ibrahim, and N. S. P. Kong, “Brightness preserving dynamic histogram equalization for image contrast enhancement,” IEEE Trans. Consumer Electron., Vol. 53, no. 4, pp. 1752–8, Jan. 2007.
- N. M. Salem, and A. K. Nandi, “Novel and adaptive contribution of the red channel in pre-processing of colour fundus images,” J. Franklin Inst., Vol. 344, no. 3, pp. 243–56, Jul. 2007.
- M. H. A. Hijazi, F. Coenen, and Y. Zheng, “Retinal image classification using a histogram based approach,” in Proceedings of International Joint Conference on Neural Networks, Barcelona, Jul. 2010, pp. 1–7.
- W. L. Yun, U. R. 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,” Inform. Sci., Vol. 178, no. 1, pp. 106–21, Jan. 2008.
- P. Feng, Y. Pan, B. Wei, W. Jin, and D. Mi, “Enhancing retinal image by the Contourlet transform,” Pattern Recog. Lett., 28, no. 4, pp. 516–22, Mar. 2007.
- T. Spencer, J. A. Olson, K. C. McHardy, K. C. P. S Sharp, and J.V. Forrester, “An image processing strategy for the segmentation and quantification in fluorescein angiograms of the ocular fundus,” Comput. Biomed. Res., Vol. 29, no. 4, pp. 284–302, Aug. 1996.
- A. J. Frame, P.E. Undrill, M. J. Cree, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester, “A comparison of computer based classification methods applied to the detection of microaneurysms in ophthalmic fluorescein angiograms,” Comput. Biol. Med., Vol. 28, no. 3, pp. 225–38, May 1998.
- M. J. Cree, E. Gamble, and D. Cornforth, “Color normalisation to reduce inter-patient and intra-patient variability in microaneurysm detection in color retinal images,” in Workshop on Digital Image Computing, Brisbane, 2005, pp. 163–8.
- M. Niemeijer, B. V. Ginneken, J. Staal, M. S. A. Suttorp-Schulten, and M. D. Abramoff, “Automatic detection of red lesions in digital color fundus photographs,” IEEE Trans. Med. Imaging, Vol. 24, no. 5, pp. 584–92, May 2005.
- M. S. Miri, and A. Mahloojifar, “A comparison study to evaluate retinal image enhancement techniques,” in Proceedings of IEEE International Conference on Signal and Image Processing Applications, Kuala Lumpur, 2009, pp. 90–4.
- M. Foracchia, E. Grisan, and A. Ruggeri, “Luminosity and contrast normalization in retinal images,” Med. Image Anal., Vol. 9, no. 3, pp. 179–90, Jun. 2005.
- M. García, M. I. López, D. Alvarez, and R. Hornero, “Assessment of four neural network based classifiers to automatically detect red lesions in retinal images,” Med. Eng. Phys., Vol. 32, no. 2, pp. 1085–93, Dec. 2010.
- B. Antal, I. Lazar, A. Hajdu, and Z. Torok, “A multi-level ensemble-based system for detecting microaneurysms in fundus images,” in International Workshop on Soft Computing Applications, Jul. 15–17, 2010, pp. 137–42.
- N. S. Datta, H.S. Dutta, M. De, and S. Mondal, “An effective approach: image quality enhancement for microaneurysms detection of non-dilated retinal fundus image,” in Proceedings of International Conference on Computational Intelligence Procedia Technology, Vol. 10, Kalyani, 2013, pp. 731–7.
- C. V. Jawahar, and A. K. Ray, “Incorporation of gray-level imprecision in representation and processing of digital images,” Pattern Recog. Lett., Vol. 17, no. 5, pp. 541–6, May 1996.
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans. Image Process., Vol. 13, no. 4, pp. 600–12, Apr. 2004.
- Y. Yang, Z. Su, and L. Sun, “Medical image enhancement algorithm based on wavelet transform,” Electron. Lett., Vol. 46, no. 2, pp. 120–1, Jan. 2010.
- A. Mead, S. Burnett, and C. Davey, “Diabetic retinopathy screen in the UK,” J. R. Soc. Med., Vol. 94, no. 3, pp. 127–9, Mar. 2001.