NDLI: Automated glaucoma detection using quasi-bivariate variational mode decomposition from fundus images

Content Provider	IET Digital Library
Author	Agrawal, Dheeraj Kumar Kirar, Bhupendra Singh Pachori, Ram Bilas
Abstract	Glaucoma is a critical and irreversible neurodegenerative eye disorder caused by damaging optical nerve head due to increased intra-ocular pressure within the eye. Detection of glaucoma is a critical job for ophthalmologists. This study presents a novel and more accurate method for automated glaucoma detection using quasi-bivariate variational mode decomposition (QB-VMD) from digital fundus images. In total, 505 fundus images are decomposed using QB-VMD method which gives band limited sub-band images (SBIs) centred around a particular frequency. These SBIs are smooth and free from mode mixing problems. The glaucoma detection accuracy depends on the most useful features as it captured appropriate information. Seventy features are extracted from QB-VMD SBIs. Extracted features are normalised and selected using ReliefF method. Selected features are then fed to singular value decomposition to reduce their dimensionality. Finally, the reduced features are classified using least square support vector machine classifier. The obtained glaucoma detection accuracies are 85.94 and 86.13% using three- and ten-fold cross validation, respectively. Obtained results are better than the existing. It may become a suitable method for ophthalmologists to examine eye disease more accurately using fundus images.
Starting Page	2401
Ending Page	2408
Page Count	8
ISSN	17519659
Volume Number	13
e-ISSN	17519667
Issue Number	Issue 13, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/13/13
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0036
Journal	IET Image Processing
Publisher Date	2019-05-03
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automated Glaucoma Detection Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging Computer Vision And Image Processing Technique Critical Eye Disorder Digital Fundus Image Diseases Extracted Feature Eye Eye Disease Feature Extraction Feature Selection Glaucoma Detection Accuracies Glaucoma Detection Accuracy Intraocular Pressure Irreversible Neurodegenerative Eye Disorder Knowledge Engineering Technique Least Square Support Vector Machine Classifier Least Squares Approximation Medical Image Processing Mode Mixing Problem Optical And Laser Radiation Optical Nerve Head Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Physiological Optics Physiological Vision QB-VMD Method QB-VMD SBIs Quasibivariate Variational Mode Decomposition ReliefF Method Singular Value Decomposition Subband Image Support Vector Machine Vision Defects
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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