Towards an automatic clinical classification of age-related macular degeneration

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1007/978-3-319-20801-5_38
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FormatText, Book 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
Pages352–359
Subjectage-related macular degeneration; fundus photography; automatic grading system; texture analysis; support vector machine
Abstract
Age-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
Peer reviewedYes
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NPARC number23001000
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Record created2016-11-25
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