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

Thanh Vân Phan, Lama Séoud, Hadi Chakor and Farida Cheriet

Article (2016)

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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.

Subjects: 1900 Biomedical engineering > 1900 Biomedical engineering
2700 Information technology > 2706 Software engineering
Department: Department of Computer Engineering and Software Engineering
Institut de génie biomédical
Funders: Diagnos Inc., CRSNG/NSERC
PolyPublie URL: https://publications.polymtl.ca/3507/
Journal Title: Journal of Ophthalmology (vol. 2016)
Publisher: Hindawi
DOI: 10.1155/2016/5893601
Official URL: https://doi.org/10.1155/2016/5893601
Date Deposited: 09 Jan 2019 12:33
Last Modified: 05 Apr 2024 17:15
Cite in APA 7: Phan, T. V., Séoud, L., Chakor, H., & Cheriet, F. (2016). Automatic screening and grading of age-related macular degeneration from texture analysis of fundus images. Journal of Ophthalmology, 2016, 1-11. https://doi.org/10.1155/2016/5893601

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