Automated glaucoma detection using quasi‐bivariate variational mode decomposition from fundus images. Issue 13 (1st November 2019)

• Record Type: Journal Article
• Authors: Agrawal, Dheeraj Kumar; Kirar, Bhupendra Singh; Pachori, Ram Bilas
• Is Part Of: IET image processing. Volume 13:Issue 13(2019)
• Page Start: 2401
• Page End: 2408
• Publication Date: 2019-11-01
• Subjects: eye -- medical image processing -- singular value decomposition -- diseases -- feature extraction -- least squares approximations -- support vector machines -- biomedical optical imaging -- vision defects -- feature selection; automated glaucoma detection -- quasibivariate variational mode decomposition -- critical eye disorder -- irreversible neurodegenerative eye disorder -- digital fundus images -- QB‐VMD method -- mode mixing problems -- glaucoma detection accuracy -- QB‐VMD SBIs -- extracted features -- ReliefF method -- singular value decomposition -- optical nerve head -- intraocular pressure -- subband images -- least square support vector machine classifier -- glaucoma detection accuracies -- eye disease; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.0036
• Languages: English
• ISSNs: 1751-9659

Abstract

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.