Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images
Mookiah, Muthu Rama Krishnan, Acharya, U. Rajendra, Koh, Joel E.W., Chandran, Vinod, Chua, Chua Kuang, Tan, Jen Hong, Lim, Choo Min, Ng, E.Y.K., Noronha, Kevin, Tong, Louis, & Laude, Augustinus (2014) Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images. Computers in Biology and Medicine, 53, pp. 55-64.
Age-related Macular Degeneration (AMD) is one of the major causes of vision loss and blindness in ageing population. Currently, there is no cure for AMD, however early detection and subsequent treatment may prevent the severe vision loss or slow the progression of the disease. AMD can be classified into two types: dry and wet AMDs. The people with macular degeneration are mostly affected by dry AMD. Early symptoms of AMD are formation of drusen and yellow pigmentation. These lesions are identified by manual inspection of fundus images by the ophthalmologists. It is a time consuming, tiresome process, and hence an automated diagnosis of AMD screening tool can aid clinicians in their diagnosis significantly. This study proposes an automated dry AMD detection system using various entropies (Shannon, Kapur, Renyi and Yager), Higher Order Spectra (HOS) bispectra features, Fractional Dimension (FD), and Gabor wavelet features extracted from greyscale fundus images. The features are ranked using t-test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance (CBBD), Receiver Operating Characteristics (ROC) curve-based and Wilcoxon ranking methods in order to select optimum features and classified into normal and AMD classes using Naive Bayes (NB), k-Nearest Neighbour (k-NN), Probabilistic Neural Network (PNN), Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The performance of the proposed system is evaluated using private (Kasturba Medical Hospital, Manipal, India), Automated Retinal Image Analysis (ARIA) and STructured Analysis of the Retina (STARE) datasets. The proposed system yielded the highest average classification accuracies of 90.19%, 95.07% and 95% with 42, 54 and 38 optimal ranked features using SVM classifier for private, ARIA and STARE datasets respectively. This automated AMD detection system can be used for mass fundus image screening and aid clinicians by making better use of their expertise on selected images that require further examination.
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|Item Type:||Journal Article|
|Keywords:||Age-related Macular Degeneration, Entropy, Texture, Higher order spectra, Gabor wavelet, Computer aided diagnosis|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 Elsevier Ltd|
|Copyright Statement:||This is the author’s version of a work that was accepted for publication in Computers in Biology and Medicine. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers in Biology and Medicine, [VOL 53, (2014)] DOI: 10.1016/j.compbiomed.2014.07.015|
|Deposited On:||28 Aug 2014 01:05|
|Last Modified:||14 Nov 2015 02:05|
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