Automatic screening and grading of age-related macular degeneration from texture analysis of fundus images

From National Research Council Canada

Download
  1. (PDF, 2 MB)
DOIResolve DOI: https://doi.org/10.1155/2016/5893601
AuthorSearch for: ; Search for: 1; Search for: ; Search for:
Name affiliation
  1. National Research Council of Canada. Information and Communication Technologies
FormatText, Article
Journal titleJournal of Ophthalmology
ISSN2090-004X
2090-0058
Volume2016
Article number5893601
Pages# of pages: 11
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.
Publication date
PublisherHindawi Publishing Corporation
LanguageEnglish
Peer reviewedYes
NPARC number23000990
Export citationExport as RIS
Report a correctionReport a correction
Record identifier0cbb5c28-7c1a-48bc-8cee-a33dade6f153
Record created2016-11-25
Record modified2020-06-02
Report a problem on this page
Date modified: