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.
Glaucoma is one of the main eye diseases; it cause progressive deterioration of optic nerve fibers due to increased fluid pressure. The existing methods of glaucoma diagnosis are time consuming, expensive and require practiced clinicians to understand the eye problems. Hence fast, cheap and more accurate glaucoma diagnosis methods are needed. This paper presents an innovative idea for diagnosis of glaucoma using third level two dimensional discrete wavelet transform (2D DWT) and histogram features from fundus images. The 2D DWT is used to decompose the glaucoma and healthy images and histogram features are extracted from 2D DWT decomposed sub band images. The least square support vector machine (LS-SVM) is used as a classifier which classifies the glaucoma and healthy images using the extracted features. The proposed method yielded classification accuracy of 88.33%, 87.50%, and 86.67% for ten, eight and fivefold cross validation respectively. The obtained classification accuracy, sensitivity and specificity are 88.33%, 90.00%, and 85.00% for tenfold cross validation respectively. Obtained results prove that the performance of the proposed method is better compared to the existing methods. It may considerably increases the diagnosis speed of ophthalmologists.
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