title: Automatic Screening and Grading of Age-Related Macular Degeneration from Texture Analysis of Fundus Images.
abstract: Age-related macular degeneration (AMD) is a disease which causes visual deficiency and irreversible blindness to the elderly. In this paper, an automatic classification method for AMD is proposed to perform robust and reproducible assessments in a telemedicine context. First, a study was carried out to highlight the most relevant features for AMD characterization based on texture, color, and visual context in fundus images. A support vector machine and a random forest were used to classify images according to the different AMD stages following the AREDS protocol and to evaluate the features' relevance. Experiments were conducted on a database of 279 fundus images coming from a telemedicine platform. The results demonstrate that local binary patterns in multiresolution are the most relevant for AMD classification, regardless of the classifier used. Depending on the classification task, our method achieves promising performances with areas under the ROC curve between 0.739 and 0.874 for screening and between 0.469 and 0.685 for grading. Moreover, the proposed automatic AMD classification system is robust with respect to image quality.
authors.affiliation: Biomedical Engineering Institute of École Polytechnique de Montréal, Montréal, QC, Canada H3C 3A7; Université Libre de Bruxelles, 1050 Brussels, Belgium.
authors.forename: Thanh Vân
authors.initials: TV
authors.lastname: Phan
authors.affiliation: Diagnos Inc., Brossard, QC, Canada J4Z 1A7.
authors.forename: Lama
authors.initials: L
authors.lastname: Seoud
authors.affiliation: Diagnos Inc., Brossard, QC, Canada J4Z 1A7.
authors.forename: Hadi
authors.initials: H
authors.lastname: Chakor
authors.affiliation: Department of Computer and Software Engineering of École Polytechnique de Montréal, Montréal, QC, Canada H3C 3A7.
authors.forename: Farida
authors.initials: F
authors.lastname: Cheriet