DMCA
Automatic Multiresolution Age-related Macular Degeneration Detection from Fundus Images
BibTeX
@MISC{Garnier_automaticmultiresolution,
author = {Mickaël Garnier and Thomas Hurtut and Houssem Ben Tahar and Farida Cheriet},
title = {Automatic Multiresolution Age-related Macular Degeneration Detection from Fundus Images},
year = {}
}
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Abstract
Age-related Macular Degeneration (AMD) is a leading cause of legal blindness. As the disease progress, visual loss occurs rapidly, therefore early diagnosis is required for timely treatment. Automatic, fast and robust screening of this widespread disease should allow an early detection. Most of the automatic diagnosis methods in the literature are based on a complex segmentation of the drusen, targeting a specific symptom of the disease. In this paper, we present a preliminary study for AMD detection from color fundus photographs using a multiresolution texture analysis. We analyze the texture at several scales by using a wavelet decomposition in order to identify all the relevant texture patterns. Textural information is cap-tured using both the sign and magnitude components of the completed model of Local Binary Patterns. An image is finally described with the textural pattern distributions of the wavelet coefficient images obtained at each level of decomposition. We use a Linear Discriminant Analysis for feature dimension reduction, to avoid the curse of dimensionality problem, and image classification. Experiments were conducted on a dataset containing 45 images (23 healthy and 22 diseased) of variable quality and captured by different cameras. Our method achieved a recognition rate of 93.3%, with a specificity of 95.5 % and a sensitivity of 91.3%. This approach shows promising results at low costs that in agreement with medical experts as well as robustness to both image quality and fundus camera model.
Keyphrases
fundus image linear discriminant analysis visual loss feature dimension reduction fundus camera model several scale wavelet decomposition preliminary study image classification amd detection specific symptom variable quality recognition rate magnitude component different camera color fundus photograph textural information robust screening timely treatment early diagnosis multiresolution texture analysis medical expert disease progress local binary pattern automatic diagnosis method wavelet coefficient image textural pattern distribution low cost complex segmentation widespread disease dimensionality problem age-related macular degeneration legal blindness early detection relevant texture pattern image quality promising result
