Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Muthu Rama Krishnan Mookiah; U Rajendra Acharya; Joel E W Koh; Vinod Chandran; Chua Kuang Chua; Jen Hong Tan; Choo Min Lim; E Y K Ng; Kevin Noronha; Louis Tong; Augustinus Laude

doi:10.1016/j.compbiomed.2014.07.015

Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images

Comput Biol Med. 2014 Oct:53:55-64. doi: 10.1016/j.compbiomed.2014.07.015. Epub 2014 Jul 30.

Authors

Affiliations

¹ Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore. Electronic address: mkm2@np.edu.sg.
² Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, 50603, Malaysia.
³ Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore.
⁴ School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia.
⁵ School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.
⁶ Department of E&C, St. Francis Institute of Technology, Mumbai, 400103, India.
⁷ Singapore National Eye Center, Singapore 168751, Singapore; Ocular Surface Research Group, Siganpore Eye Research Institute, Singapore 168751, Singapore; Duke-NUS Graduate Medical School, Singapore 169857, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore.
⁸ National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore 308433, Singapore.

PMID: 25127409
DOI: 10.1016/j.compbiomed.2014.07.015

Abstract

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.

Keywords: Age-related Macular Degeneration; Computer aided diagnosis; Entropy; Gabor wavelet; Higher order spectra; Texture.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Databases, Factual
Diagnostic Techniques, Ophthalmological*
Fundus Oculi
Humans
Image Interpretation, Computer-Assisted / methods*
Macular Degeneration / diagnosis*
Models, Statistical
Wavelet Analysis