Towards an automatic clinical classification of age-related macular degeneration

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DOIResolve DOI: http://doi.org/10.1007/978-3-319-20801-5_38
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TypeBook Chapter
Book titleImage Analysis and Recognition
Series titleLecture Notes in Computer Science; Volume 9164
ISSN0302-9743
1611-3349
ISBN978-3-319-20800-8
978-3-319-20801-5
Pages352359
Subjectage-related macular degeneration; fundus photography; automatic grading system; texture analysis; support vector machine
AbstractAge-related macular degeneration (AMD) is the leading cause of visual deficiency and irreversible blindness for elderly individuals in Western countries. Its screening relies on human analysis of fundus images which often leads to inter- and intra-expert variability. With the aim of developing an automatic grading system for AMD, this paper focuses on identifying the best features for automatic detection of AMD in fundus images. First, different features based on local binary pattern (LBP), run-length matrix, color or gradient information are computed. Then, a feature selection is applied for dimensionality reduction. Finally, a support vector machine is trained to determine the presence or absence of AMD. Experiments were conducted on a dataset of 140 fundus images. A classification performance with an accuracy of 96 % is achieved on preprocessed images of macula area using LBP features.
Publication date
PublisherSpringer
LanguageEnglish
AffiliationNational Research Council Canada
Peer reviewedYes
NRC publication
This is a non-NRC publication

"Non-NRC publications" are publications authored by NRC employees prior to their employment by NRC.

NPARC number23001000
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Record identifiere884c16b-44fa-4f86-9efe-ede18f28905e
Record created2016-11-25
Record modified2016-11-25
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