RAMA KRISHNAN M, Muthu and FAUST, Oliver (2012). Automated Glaucoma Detection Using Hybrid Feature Extraction in Retinal Fundus Images. Journal of Mechanics in Medicine and Biology, 13 (1), 1350011-1350032.
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Abstract
Glaucoma is one of the most common causes of blindness. Robust mass screening may help to extend the symptom-free life for affected patients. To realize mass screening requires a cost-effective glaucoma detection method which integrates well with digital medical and administrative processes. To address these requirements, we propose a novel low cost automated glaucoma diagnosis system based on hybrid feature extraction from digital fundus images. The paper discusses a system for the automated identification of normal and glaucoma classes using higher order spectra (HOS), trace transform (TT), and discrete wavelet transform (DWT) features. The extracted features are fed to a support vector machine (SVM) classifier with linear, polynomial order 1, 2, 3 and radial basis function (RBF) in order to select the best kernel for automated decision making. In this work, the SVM classifier, with a polynomial order 2 kernel function, was able to identify glaucoma and normal images with an accuracy of 91.67%, and sensitivity and specificity of 90% and 93.33%, respectively. Furthermore, we propose a novel integrated index called Glaucoma Risk Index (GRI) which is composed from HOS, TT, and DWT features, to diagnose the unknown class using a single feature. We hope that this GRI will aid clinicians to make a faster glaucoma diagnosis during the mass screening of normal/glaucoma images.
Item Type: | Article |
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Identification Number: | https://doi.org/10.1142/S0219519413500115 |
Page Range: | 1350011-1350032 |
Depositing User: | Oliver Faust |
Date Deposited: | 15 Aug 2017 08:31 |
Last Modified: | 18 Mar 2021 05:55 |
URI: | https://shura.shu.ac.uk/id/eprint/11444 |
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