Iterative variational mode decomposition based automated detection of glaucoma using fundus images

Bhandary, Sulatha V (2017) Iterative variational mode decomposition based automated detection of glaucoma using fundus images. Computers in Biology and Medicine, 88. pp. 142-149. ISSN 0010-4825

[img] PDF
RMS - 00004459.pdf - Published Version
Restricted to Registered users only

Download (2MB) | Request a copy

Abstract

Glaucoma is one of the leading causes of permanent vision loss. It is an ocular disorder caused by increased fluid pressure within the eye. The clinical methods available for the diagnosis of glaucoma require skilled supervision. They are manual, time consuming, and out of reach of common people. Hence, there is a need for an automated glaucoma diagnosis system for mass screening. In this paper, we present a novel method for an automated diagnosis of glaucoma using digital fundus images. Variational mode decomposition (VMD) method is used in an iterative manner for image decomposition. Various features namely, Kapoor entropy, Renyi entropy, Yager entropy, and fractal dimensions are extracted from VMD components. ReliefF algorithm is used to select the discriminatory features and these features are then fed to the least squares support vector machine (LS-SVM) for classification. Our proposed method achieved classification accuracies of 95:19% and 94:79% using three-fold and ten-fold cross-validation strategies, respectively. This system can aid the ophthalmologists in confirming their manual reading of classes (glaucoma or normal) using fundus images.

Item Type: Article
Uncontrolled Keywords: Glaucoma; variation mode decomposition; entropy; fractal dimension; least squares support vector machine.
Subjects: Medicine > KMC Manipal > Ophthalmology
Depositing User: KMC Library
Date Deposited: 31 May 2018 04:01
Last Modified: 31 May 2018 04:01
URI: http://eprints.manipal.edu/id/eprint/151210

Actions (login required)

View Item View Item