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.

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 : a journal of the British Diabetic Association, 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

URL : http://doi.org/10.3390/s90302148

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

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

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

S. Lee, M. D. Abramoff, and J. M. Reinhardt, Retinal atlas statistics from color fundus images, Medical Imaging 2010: Image Processing, p.762310, 2010.
DOI : 10.1117/12.843714

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, vol.17, issue.8, 2011.
DOI : 10.1089/tmj.2011.0004

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

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3171781

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

M. Blanco, M. G. Penedo, N. Barreira, M. Penas, and M. J. Carreira, Localization and Extraction of the Optic Disc Using the Fuzzy Circular Hough Transform, Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing, ser. ICAISC'06, pp.712-721, 2006.
DOI : 10.1007/11785231_74

A. Frangi, W. Niessen, K. Vincken, and M. Viergever, Multiscale vessel enhancement filtering, pp.130-137, 1998.
DOI : 10.1148/radiology.191.1.8134563

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

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

A. Depeursinge, A. Foncubierta-rodriguez, D. Van-de-ville, and H. Müller, Lung Texture Classification Using Locally???Oriented Riesz Components, Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention -Volume Part III, ser. MICCAI'11, pp.231-238, 2011.
DOI : 10.1109/TIP.2009.2038832

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

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.86-693, 2013.
DOI : 10.1117/12.2006357

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

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

J. Davis and M. Goadrich, The relationship between Precision-Recall and ROC curves, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.233-240, 2006.
DOI : 10.1145/1143844.1143874