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TeleOphta: Machine learning and image processing methods for teleophthalmology |
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| Authors: | E Decencière G Cazuguel X Zhang G Thibault J-C Klein F Meyer B Marcotegui G Quellec M Lamard R Danno D Elie P Massin Z Viktor A Erginay B Laÿ A Chabouis |
| Institution: | 1. Center for mathematical morphology, systems and mathematics department, MINES ParisTech, 35, rue St-Honoré, 77300 Fontainebleau, France;2. Service d’ophtalmologie, hôpital Lariboisière, AP–HP, 2, rue Ambroise-Paré, 75475 Paris cedex 10, France;3. Direction de la politique médicale, parcours des patients et organisations médicales innovantes – télémédecine, Assistance publique–Hôpitaux de Paris, 3, avenue Victoria, 75184 Paris cedex 04, France;4. Inserm UMR 1101 LaTIM, bâtiment I, CHRU Morvan, 29200 Brest, France;5. ADCIS, 3, rue Martin-Luther-King, 14280 Saint-Contest, France;6. Télécom Bretagne, Institut Mines-Télécom, ITI Department, 29200 Brest, France;7. Université de Brest, Inserm UMR 1101 LaTIM, SFR ScInBioS, 29200 Brest, France |
| Abstract: | A complete prototype for the automatic detection of normal examinations on a teleophthalmology network for diabetic retinopathy screening is presented. The system combines pathological pattern mining methods, with specific lesion detection methods, to extract information from the images. This information, plus patient and other contextual data, is used by a classifier to compute an abnormality risk. Such a system should reduce the burden on readers on teleophthalmology networks. |
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