Automated glaucoma detection using quasi‐bivariate variational mode decomposition from fundus images

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.

FGS-HDNN: Fractional Gravitational Search based Hybrid Deep Neural Network for Glaucoma Detection using Fundus Images

“…The new more exact approach for the detection of computerized glaucoma recognition by quasi-bivariate variational mode decomposition (QB-VMD) was proposed by Agrawal DK et al [15]. RIM-1 database consists of the MIAG database evaluated on 505 images, in those 255 healthy images and 250 glaucoma infected images.…”

“…The pre-processing scheme improves the important aspects for further processing. It includes the schemes like image resizing, channel extraction, noise removal, image enhancement is employed [29] and the preprocessed image is shown in Fig 2.…”

Short-Term PV Power Prediction Based on Optimized VMD and LSTM

“…In 2014, Dragomiretskiy et al [27] proposed the VMD algorithm. Because of the strong theoretical background, the VMD is widely applied in the many fields such as fault diagnosis [30][31], image processing [32] and signal processing [33][34] The mode number ( k ) and penalty factor (  ) determine if the decomposed result exists the information loss and over decomposition [35]. Thus, the two key parameters play an important role for the separation of complexed components of PV power.…”

Glaucoma Detection using Bi-Dimensional Empirical Mode Decomposition from Retinal Fundus Images

“…Robust features were found out using post-processing, and then classified by SVM. Agrawal [16] designed an automated glaucoma detection using quasi-bivariate variational mode decomposition (QB-VMD). Robust features were found out using post-processing, and then classified by SVM.…”