R. Phillips, J. Forrester, and P. Sharp, Automated detection and quantification of retinal exudates, Graefe's Archive for, Clinical and Experimental Ophthalmology, vol.231, pp.90-94, 1993.

B. Ege, O. Larsen, and O. Hejlesen, Detection of abnormalities in retinal images using digital image analysis, pp.833-840, 1999.

C. Sinthanayothin, J. F. Boyce, T. H. Williamson, H. L. Cook, E. Mensah et al., Automated detection of diabetic retinopathy on digital fundus images, Diabetic medicine, pp.105-112, 2002.
DOI : 10.1046/j.1464-5491.2002.00613.x

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, issue.3, pp.2148-2161, 2009.
DOI : 10.3390/s90302148

X. Zhang and O. Chutatape, Detection and classification of bright lesions in color fundus images, 2004 International Conference on Image Processing, 2004. ICIP '04., pp.139-142, 2004.
DOI : 10.1109/ICIP.2004.1418709

M. Niemeijer, B. Van-ginneken, S. R. Russell, M. S. Suttorp-schulten, and M. D. Abramoff, Automated Detection and Differentiation of Drusen, Exudates, and Cotton-Wool Spots in Digital Color Fundus Photographs for Diabetic Retinopathy Diagnosis, Investigative Opthalmology & Visual Science, vol.48, issue.5, pp.2260-2267, 2007.
DOI : 10.1167/iovs.06-0996

M. García, C. I. Sánchez, M. I. López, D. Abásolo, and R. Hornero, Neural network based detection of hard exudates in retinal images, Computer Methods and Programs in Biomedicine, vol.93, issue.1, pp.9-19, 2009.
DOI : 10.1016/j.cmpb.2008.07.006

L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y. Li, K. W. Tobin et al., Automatic retina exudates segmentation without a manually labelled training set, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.1396-1400, 2011.
DOI : 10.1109/ISBI.2011.5872661

URL : https://hal.archives-ouvertes.fr/hal-00585177

B. Dupas, T. Walter, A. Erginay, R. Ordonez, N. Deb-joardar et al., Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy, Diabetes & Metabolism, vol.36, issue.3, pp.213-220, 2010.
DOI : 10.1016/j.diabet.2010.01.002

URL : https://hal.archives-ouvertes.fr/hal-00836043

A. Feroui, M. Messadi, I. Hadjidj, and A. Bessaid, NEW SEGMENTATION METHODOLOGY FOR EXUDATE DETECTION IN COLOR FUNDUS IMAGES, Journal of Mechanics in Medicine and Biology, vol.13, issue.01, p.1350014, 2013.
DOI : 10.1142/S0219519413500140

L. Giancardo, F. Meriaudeau, T. P. Karnowski, Y. Li, S. Garg et al., Exudate-based diabetic macular edema detection in fundus images using publicly available datasets, Medical Image Analysis, vol.16, issue.1, pp.216-226, 2012.
DOI : 10.1016/j.media.2011.07.004

URL : https://hal.archives-ouvertes.fr/hal-00639756

H. F. Jaafar, A. K. Nandi, and W. , Al-Nuaimy, Automated detection of red lesions from digital colour fundus photographs, Conf Proc IEEE Eng Med Biol Soc, pp.6232-6237, 2011.

M. I. Lopez, C. Sanchez, and R. Hornero, Retinal image analysis to detect and quantify lesions associated with diabetic retinopathy, 2003.

R. R. Bourne, Ethnicity and ocular imaging, Eye, vol.46, issue.3, pp.297-300, 2011.
DOI : 10.1038/eye.2010.187

Y. Li, T. Karnowski, K. Tobin, L. Giancardo, S. Morris et al., A health insurance portability and accountability act? compliant ocular telehealth network for the remote diagnosis and management of diabetic retinopathy, Telemedicine and e-Health

H. Bay, A. Ess, T. Tuytelaars, and L. Van-gool, Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, vol.110, issue.3, pp.346-359, 2008.
DOI : 10.1016/j.cviu.2007.09.014

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.738

D. G. Lowe, Distinctive image features from scaleinvariant keypoints, Int. J. Comp. Vis, pp.91-110, 2004.
DOI : 10.1023/b:visi.0000029664.99615.94

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.14.4931

A. Can, H. Shen, J. N. Turner, H. L. Tanenbaum, and B. Roysam, Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms, IEEE Transactions on Information Technology in Biomedicine, vol.3, issue.2, pp.125-138, 1999.
DOI : 10.1109/4233.767088

F. L. Bookstein, Principal warps: thin-plate splines and the decomposition of deformations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.11, issue.6, pp.567-585, 1989.
DOI : 10.1109/34.24792

C. I. Sánchez, M. García, A. Mayo, M. I. López, and R. Hornero, Retinal image analysis based on mixture models to detect hard exudates, Medical Image Analysis, vol.13, issue.4, pp.650-658, 2009.
DOI : 10.1016/j.media.2009.05.005

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, issue.8, pp.720-727, 2011.
DOI : 10.1016/j.compmedimag.2008.08.009

N. Otsu, A Threshold Selection Method from Gray-Level Histograms, IEEE Transactions on Systems, Man, and Cybernetics, vol.9, issue.1, pp.62-66, 1979.
DOI : 10.1109/TSMC.1979.4310076

M. Foracchia, E. Grisan, and A. Ruggeri, Luminosity and contrast normalization in retinal images, Medical Image Analysis, vol.9, issue.3, pp.179-190, 2005.
DOI : 10.1016/j.media.2004.07.001

R. A. Kirsch, Computer determination of the constituent structure of biological images, Computers and Biomedical Research, vol.4, issue.3, pp.315-328, 1970.
DOI : 10.1016/0010-4809(71)90034-6

U. Kthe and M. Felsberg, Riesz-transforms vs. derivatives: On the relationship between the boundary tensor and the energy tensor, Proc. Scale Space Conference (this, pp.179-191, 2005.

R. A. Kirsch, Computer determination of the constituent structure of biological images, Computers and Biomedical Research, vol.4, issue.3, pp.315-328, 1970.
DOI : 10.1016/0010-4809(71)90034-6

W. T. Freeman and E. H. Adelson, The design and use of steerable filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.9, pp.891-906, 1991.
DOI : 10.1109/34.93808

M. Unser and D. Van-de-ville, Wavelet Steerability and the Higher-Order Riesz Transform, IEEE Transactions on Image Processing, vol.19, issue.3, pp.636-652, 2010.
DOI : 10.1109/TIP.2009.2038832

S. Ali, K. M. Adal, D. Sidibé, E. Chaum, T. P. Karnowski et al., Steerable wavelet transform for atlas based retinal lesion segmentation, Medical Imaging 2013: Image Processing, pp.86693-86703, 2013.
DOI : 10.1117/12.2006357

URL : https://hal.archives-ouvertes.fr/hal-00784561