Cite

[1] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, S. Süsstrunk, SLIC super-pixels compared to state-of-the-art superpixel methods, IEEE Trans. PAMI34 (2012) 2274–2282. ⇒7010.1109/TPAMI.2012.120Search in Google Scholar

[2] K. Adem, Exudate detection for diabetic retinopathy with circular Hough transformation and convolutional neural networks, Expert Syst. Appl.114 (2018) 289–295. ⇒6710.1016/j.eswa.2018.07.053Search in Google Scholar

[3] C. Agurto, V. Murray, H. Yu, J. Wigdahl, M. Pattichis, S. Nemeth, S. Barriga, P. Soliz, A multiscale optimization approach to detect exudates in the macula, IEEE J. Biomed. Health Inf.18, 4 (2014) 1328-1337. ⇒6710.1109/JBHI.2013.2296399Search in Google Scholar

[4] K. S. Deepak, J. Sivaswamy, Automatic assessment of macular edema from color retinal images, IEEE Trans. Med. Imag.31, 3 (2012) 766-776. ⇒6710.1109/TMI.2011.2178856Search in Google Scholar

[5] M. Esmaeili, H. Rabbani, A. M. Dehnavi, A. Dehghani, Automatic detection of exudates and optic disc in retinal images using curvelet transform, IET Image Proc.6 (2012) 1005–1013. ⇒6710.1049/iet-ipr.2011.0333Search in Google Scholar

[6] L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y. Q. Li, S. Garg, K. W. Tobin Jr., E. Chaum, Exudate-based diabetic macular edema detection in fundus images using publicly available datasets, Med. Image Anal.16, 1 (2012) 216–226. ⇒6710.1016/j.media.2011.07.004Search in Google Scholar

[7] C. E. Hann, J. A. Revie, D. Hewett, J. G. Chase, G. M. Shaw, Screening for diabetic retinopathy using computer vision and physiological markers, J. Diabetes Sci. Technol.3, 4 (2009) 819–834. ⇒6710.1177/193229680900300431Search in Google Scholar

[8] B. Harangi, A. Hajdú, Automatic exudate detection by fusing multiple active contours and regionwise classification, Comput. Biol. Med.54 (2014) 156–171. ⇒6710.1016/j.compbiomed.2014.09.001Search in Google Scholar

[9] S. Joshi, P. T. Kerule, A review on exudates detection methods for diabetic retinopathy, Biomed. Pharmacoter.97 (2018) 1454–1460. ⇒6710.1016/j.biopha.2017.11.009Search in Google Scholar

[10] J. Kaur, D. Mittal, A generalized method for the segmentation of exudates from pathological retinal fundus images, Biocybern. Biomed. Eng.38, 1 (2018) 27–53. ⇒6710.1016/j.bbe.2017.10.003Search in Google Scholar

[11] P. Khojasteh, L. A. Passos Júnior, T. Carvalho, E. Rezende, B. Aliahmad, J. P. Papa, D. K. Kumar, Exudate detection in fundus images using deeply-learnable features, Comput. Biol. Med.104 (2019) 62–69. ⇒6710.1016/j.compbiomed.2018.10.031Search in Google Scholar

[12] P. Khojasteh, B. Aliahmad, D. K. Kumar, A novel color space of fundus images for automatic exudates detection, Biomed. Sign. Proc. Control49 (2019) 240–249. ⇒6710.1016/j.bspc.2018.12.004Search in Google Scholar

[13] W. Kusakunniran, Q. Wu, P. Ritthipravat, J. Zhang, Hard exudates segmentation based on learned initial seeds and iterative graph cut, Comput. Meth. Prog. Biol.158 (2018) 173–183. ⇒6710.1016/j.cmpb.2018.02.011Search in Google Scholar

[14] J. L. Leasher, R. R. Bourne, S. R. Flaxman, J. B. Jonas, J. Keeffe, K. Naidoo, K. Pesudovs, H. Price, R. A. White, T. Y. Wong, S. Resniko, H. R. Taylor, et al., Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 1990-2010, Diabetes Care39 (2016) 1643–1649. ⇒6610.2337/dc15-2171Search in Google Scholar

[15] J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, E. Fletcher, L. Kennedy, Optic nerve head segmentation, IEEE Trans. Med. Imag.23, 2 (2005) 256-264. ⇒6710.1109/TMI.2003.823261Search in Google Scholar

[16] M. R. K. Mookiah, U. R. Acharya, C. K. Chua, C. M. Lim, E. Y. K. Ng, A. Laude, Computer-aided diagnosis of diabetic retinopathy: a review, Comput. Biol. Med.43 (2013) 2136–2155. ⇒6710.1016/j.compbiomed.2013.10.007Search in Google Scholar

[17] J. Nayak, P. S. Bhat, U. R. Acharya, C. Lim, M. Kagathi, Automated identification of different stages of diabetic retinopathy using digital fundus images, J. Med. Syst.32 (2008) 107–115. ⇒6710.1007/s10916-007-9113-9Search in Google Scholar

[18] P. Porwal, S. Pachade, R. Kamble, M. Kokare, G. Deshmukh, V. Sahasrabuddhe, F. Meriaudeau, Indian Diabetic Retinopathy Image Dataset (IDRiD): A database for diabetic retinopathy screening research, Data3, 3 (2018) 25. ⇒67, 6810.3390/data3030025Search in Google Scholar

[19] C. I. Sánchez, M. García, A. Mayo, M. I. Lopez, R. Hornero, Retinal image analysis based on mixture models to detect hard exudates, Med. Image Anal.13, 4 (2009) 650–658. ⇒67, 69, 7010.1016/j.media.2009.05.005Search in Google Scholar

[20] D. Sidibé, I. Sadek, F. Mériaudeau, Discrimination of retinal images containing bright lesions using sparse coded features and SVM, Comput. Biol. Med.62 (2015) 175–184. ⇒6710.1016/j.compbiomed.2015.04.026Search in Google Scholar

[21] R. Sohini, P. Dara, K. K. Parhi, DREAM: diabetic retinopathy analysis using machine learning, IEEE J. Biomed. Health Inf.18, 5 (2014) 1717-1729. ⇒6710.1109/JBHI.2013.2294635Search in Google Scholar

[22] L. Szilágyi, S. M. Szilágyi, B. Benyó, Efficient inhomogeneity compensation using fuzzy c-means clustering models, Comput. Meth. Prog. Biol.108 (2012) 80–89. ⇒6910.1016/j.cmpb.2012.01.005Search in Google Scholar

[23] X. Zhang, G. Thibault, E. Decencière, B. Marcotegui, B. Laÿ, R. Danno, G. Cazuguel, G. Quellec, M. Lamard, P. Massin, A. Chabouis, Z. Victor, A. Erginay, Exudate detection in color retinal images for mass screening of diabetic retinopathy, Med. Image Anal.18, 7 (2014) 1026–1043. ⇒6710.1016/j.media.2014.05.004Search in Google Scholar

eISSN:
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Language:
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