A. Youssif, A. Ghalwash, A. Abdel-rahman-ghoneim, and A. , Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter, IEEE Transactions on Medical Imaging, vol.27, issue.1, pp.11-18, 2008.
DOI : 10.1109/TMI.2007.900326

M. D. Abrmoff, M. Niemeijer, M. S. Suttorp-schulten, M. A. Viergever, S. R. Russell et al., Evaluation of a System for Automatic Detection of Diabetic Retinopathy From Color Fundus Photographs in a Large Population of Patients With Diabetes, Diabetes Care, vol.31, issue.2, pp.193-198, 2008.
DOI : 10.2337/dc07-1312

C. Agurto, E. S. Barriga, V. Murray, S. Nemeth, R. Crammer et al., Automatic Detection of Diabetic Retinopathy and Age-Related Macular Degeneration in Digital Fundus Images, Investigative Opthalmology & Visual Science, vol.52, issue.8, pp.5862-5871, 2011.
DOI : 10.1167/iovs.10-7075

S. Beucher, Numerical residues, Mathematical Morphology: 40 Years On, pp.23-32, 2005.
DOI : 10.1016/j.imavis.2006.07.020

L. Breiman, E. Decencì-ere, G. Cazuguel, X. Zhang, G. Thibault et al., Random forests TeleOphta: Machine learning and image processing methods for teleophthalmology, Machine Learning, vol.45, issue.1, pp.5-32, 2001.
DOI : 10.1023/A:1010933404324

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. Erginay, A. Chabouis, C. Viens-bitker, N. Robert, A. Lecleire-collet et al., OPHDIAT??: Quality-assurance programme plan and performance of the network, Diabetes & Metabolism, vol.34, issue.3, pp.235-242, 2008.
DOI : 10.1016/j.diabet.2008.01.004

C. Eswaran, A. Reza, and S. Hati, Extraction of the Contours of Optic Disc and Exudates Based on Marker-Controlled Watershed Segmentation, 2008 International Conference on Computer Science and Information Technology, pp.719-723, 2008.
DOI : 10.1109/ICCSIT.2008.13

A. Fleming, S. Philip, K. Goatman, G. Williams, J. Olson et al., Automated detection of exudates for diabetic retinopathy screening, Physics in medicine and biology 52, p.7385, 2007.
DOI : 10.1088/0031-9155/52/24/012

M. Foracchia, E. Grisan, and A. Ruggeri, Detection of Optic Disc in Retinal Images by Means of a Geometrical Model of Vessel Structure, IEEE Transactions on Medical Imaging, vol.23, issue.10, pp.1189-1195, 2004.
DOI : 10.1109/TMI.2004.829331

L. Gagnon, M. Lalonde, M. Beaulieu, and M. Boucher, Procedure to detect anatomical structures in optical fundus images, Medical Imaging 2001. International Society for Optics and Photonics, pp.1218-1225, 2001.

G. Gardner, D. Keating, T. Williamson, and A. Elliott, Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool., British Journal of Ophthalmology, vol.80, issue.11, pp.940-944, 1996.
DOI : 10.1136/bjo.80.11.940

L. Giancardo, F. Meriaudeau, T. 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

L. Giancardo, F. Meriaudeau, T. Karnowski, Y. Li, K. 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. Harangi, B. Antal, and A. Hajdu, Automatic exudate detection with improved na¨?vena¨?ve-bayes classifier, Computer-Based Medical Systems (CBMS), 2012 25th International Symposium on. IEEE, pp.1-4, 2012.
DOI : 10.1109/cbms.2012.6266341

T. Kauppi, V. Kalesnykiene, J. Kamarainen, L. Lensu, I. Sorri et al., the DIARETDB1 diabetic retinopathy database and evaluation protocol, Procedings of the British Machine Vision Conference 2007, pp.61-65, 2007.
DOI : 10.5244/C.21.15

M. Lalonde, M. Beaulieu, and L. Gagnon, Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching, IEEE Transactions on Medical Imaging, vol.20, issue.11, pp.1193-1200, 2001.
DOI : 10.1109/42.963823

C. Lantuéjoul and F. Maisonneuve, Geodesic methods in quantitative image analysis, Pattern Recognition, vol.17, issue.2, p.177187, 1984.

P. Massin, A. Chabouis, A. Erginay, C. Viens-bitker, A. Lecleire-collet et al., OPHDIAT??: A telemedical network screening system for diabetic retinopathy in the ??le-de-France, Diabetes & Metabolism, vol.34, issue.3, pp.227-234, 2008.
DOI : 10.1016/j.diabet.2007.12.006

M. Niemeijer, B. Van-ginneken, S. Russell, M. Suttorp-schulten, and M. Abràmoff, 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

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikitlearn: Machine learning in python, The Journal of Machine Learning Research, vol.12, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

S. Philip, A. D. Fleming, K. A. Goatman, S. Fonseca, P. Mcnamee et al., The efficacy of automated "disease/no disease" grading for diabetic retinopathy in a systematic screening programme, British Journal of Ophthalmology, vol.91, issue.11, pp.1512-1517, 2007.
DOI : 10.1136/bjo.2007.119453

C. Sánchez, M. García, A. Mayo, M. 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

C. Sánchez, M. Niemeijer, I. I?gum, A. Dumitrescu, M. Suttorp-schulten et al., Contextual computer-aided detection: Improving bright lesion detection in retinal images and coronary calcification identification in CT scans, Medical Image Analysis, vol.16, issue.1, pp.50-62, 2012.
DOI : 10.1016/j.media.2011.05.004

C. Sánchez, M. Niemeijer, M. Suttorp-schulten, M. Abrámoff, and B. Van-ginneken, Improving hard exudate detection in retinal images through a combination of local and contextual information, 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp.5-8, 2010.
DOI : 10.1109/ISBI.2010.5490429

G. S. Scotland, P. Mcnamee, S. Philip, A. D. Fleming, K. A. Goatman et al., Cost-effectiveness of implementing automated grading within the national screening programme for diabetic retinopathy in Scotland, British Journal of Ophthalmology, vol.91, issue.11, pp.1518-1523, 2007.
DOI : 10.1136/bjo.2007.120972

J. Serra, Image Analysis and Mathematical Morphology -Volume II : Theoretical Advances, 1988.

C. Sinthanayothin, J. Boyce, T. Williamson, H. Cook, E. Mensah et al., Automated detection of diabetic retinopathy on digital fundus images, Diabetic Medicine, vol.97, issue.2, 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

A. Sopharak, B. Uyyanonvara, S. Barman, and T. 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, 2008.
DOI : 10.1016/j.compmedimag.2008.08.009

K. Tobin, E. Chaum, V. Govindasamy, and T. Karnowski, Detection of Anatomic Structures in Human Retinal Imagery, IEEE Transactions on Medical Imaging, vol.26, issue.12, pp.1729-1739, 2007.
DOI : 10.1109/TMI.2007.902801

K. W. Tobin, E. Chaum, V. Govindasamy, and T. Karnowski, Detection of Anatomic Structures in Human Retinal Imagery, IEEE Transactions on Medical Imaging, vol.26, issue.12, pp.1729-1739, 2007.
DOI : 10.1109/TMI.2007.902801

D. Usher, M. Dumskyj, M. Himaga, T. Williamson, S. Nussey et al., Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening, Diabetic Medicine, vol.98, issue.1, pp.84-90, 2004.
DOI : 10.1046/j.1464-5491.2002.00613.x

T. Walter, J. Klein, P. Massin, and A. Erginay, A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina, IEEE Transactions on Medical Imaging, vol.21, issue.10, pp.1236-1243, 2002.
DOI : 10.1109/TMI.2002.806290

T. Walter and J. Klein, Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques, Proceedings of the Second International Symposium on Medical Data Analysis. ISMDA '01, p.282287, 2001.
DOI : 10.1007/3-540-45497-7_43

C. Wolf and J. Jolion, Object count/area graphs for the evaluation of object detection and segmentation algorithms, International Journal of Document Analysis and Recognition (IJDAR), vol.6, issue.4, pp.280-296, 2006.
DOI : 10.1007/s10032-006-0014-0

X. Zhang, G. Thibault, and E. Decencì-ere, application of the morphological ultimate opening to the detection of microaneurysms on eye fundus images from clinical databases, International Congress for Stereology, 2011.

X. Zhang, G. Thibault, and E. Decencì-ere, Procédé de normalisation d'´ echelle d'images ophtalmologiques (patent, filing number, pp.12-53929, 2012.

X. Zhang, G. Thibault, E. Decencì-ere, G. Quellec, G. Gazuguel et al., Spatial normalization of eye fundus images, International Symposium on Biomedical Imaging -ISBI
URL : https://hal.archives-ouvertes.fr/hal-00945417