References

Al-Thahab, O.Q.J., 2016. Speech recognition based radon-discrete cosine transforms by Delta Neural Network learning rule. Proceedings of the 2016 International Symposium on Fundamentals of Electrical Engineering (ISFEE), June 30-July 2, 2016, IEEE, Bucharest, Romania, ISBN:978-1-4673-9574-8, pp: 1-6.

Firoz, R., M.S. Ali, M.N.U. Khan, M.K. Hossain and M.K. Islam et al., 2016. Medical image enhancement using morphological transformation. J. Data Anal. Inf. Process., 4: 1-12.
CrossRef  |  

Garcia, M., C.I. Sanchez, M.I. Lopez, D. Abasolo and R. Hornero, 2009. Neural network based detection of hard exudates in retinal images. Comput. Methods Programs Biomed., 93: 9-19.
CrossRef  |  Direct Link  |  

Gonzalez, R.C., R.E. Woods and S.L. Eddins, 2009. Digital Image Processing Using MATLAP. 2nd Edn., McGraw-Hill Education, New York, USA., ISBN:978-0-07-070262-2, Pages: 738.

Hafez, M. and S.A. Azeem, 2002. Using adaptive edge technique for detecting microaneurysms in fluorescein angiograms of the ocular fundus. Proceedings of the 11th IEEE International Conference on Mediterranean Electrotechnical (IEEE Cat. No.02CH37379), May 7-9, 2002, IEEE, Cairo, Egypt, pp: 479-483.

Jaafar, H.F., A.K. Nandi and W. Al-Nuaimy, 2011. Detection of exudates from digital fundus images using a region-based segmentation technique. Proceedings of the 2011 19th European Conference on Signal Processing, August 29-September 2, 2011, IEEE, Barcelona, Spain, pp: 1020-1024.

Kande, G.B., P.V. Subbaiah and T.S. Savithri, 2008. Segmentation of exudates and optic disk in retinal images. Proceedings of the 2008 6th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP'08), December 16-19, 2008, IEEE. Bhubaneswar, India, ISBN:978-0-7695-3476-3, pp: 535-542.

Kauppi, T., K.V., J.K. Kamarainen, L. Lensu, S. Iiris and R. Asta et al., 2017. The DIARETDB1 diabetic retinopathy database and evaluation protocol. Proceedings of the 18th British Conference on Machine Vision, September 10-13, 2007, University of Warwick, Coventry, England, UK., pp: 1-10.

Kauppi, T., V. Kalesnykiene, J.K. Kamarainen, L. Lensu and I. Sorri et al., 2006. DIARETDB0: Evaluation database andmorphology for Diabetic Retinopathy algorithm. J. Opthamology, 1: 1-17.
Direct Link  |  

Meyer, F. and S. Beucher, 1990. Morphological segmentation. J. Visual Commun. Image Represent., 1: 21-46.
CrossRef  |  Direct Link  |  

Osareh, A., M. Mirmehdi, B. Thomas and R. Markham, 2002. Classification and localisation of diabetic-related eye disease. Proceedings of the European Conference on Computer Vision, May 28-31, 2002, Springer, Berlin, Germany, ISBN:978-3-540-43748-2, pp: 502-516.

Osareh, A., M. Mirmehdi, B. Thomas and R. Markham, 2003. Automated identification of diabetic retinal exudates in digital colour images. Br. J. Ophthalmol., 87: 1220-1223.
PubMed  |  Direct Link  |  

Priyanka1, S. and A.S.N. Kumar, 2016. Noise removal in remote sensing image using kalman filter algorithm. Intl. J. Adv. Res. Comput. Commun. Eng., 5: 894-897.
CrossRef  |  Direct Link  |  

Sanchez, C.I., M. Garcia, A. Mayo, M.I. Lopez and R. Hornero, 2009. Retinal image analysis based on mixture models to detect hard exudates. Med. Image Anal., 13: 650-658.
CrossRef  |  PubMed  |  Direct Link  |  

Sekhar, S., W. Al-Nuaimy and A.K. Nandi, 2008. Automated localisation of optic disk and fovea in retinal fundus images. Proceedings of the 2008 16th European Conference on Signal Processing, August 25-29, 2008, IEEE, Lausanne, Switzerland, pp: 1-5.

Sopharak, A., B. Uyyanonvara and S. Barman, 2011. Automatic microaneurysm detection from non-dilated diabetic retinopathy retinal images using mathematical morphology methods. IAENG. Intl. J. Comput. Sci., 38: 295-301.
Direct Link  |  

Sopharak, A., M.N. Dailey, B. Uyyanonvara, S. Barman and T. Williamson et al., 2010. Machine learning approach to automatic exudate detection in retinal images from diabetic patients. J. Mod. Opt., 57: 124-135.
Direct Link  |  

Walter, J., C. Klein, P. Massin and A. Erginay, 2002. A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans. Med. Imaging., 21: 1236-1243.
CrossRef  |  PubMed  |  Direct Link  |  

Walter, J., C. Klein, P. Massin and A. Erginay, 2002. A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans. Med. Imaging., 21: 1236-1243.
CrossRef  |  PubMed  |  Direct Link  |  

Weinstein, E., 2018. Circular segment. Wolfram Research, Champaign, Illinois, USA. http://mathworld.wolfram.com/CircularSegment.html

Welfer, D., J. Scharcanski and D.R. Marinho, 2010. A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images. Computerized Med. Imaging Graphics, 34: 228-235.
CrossRef  |