Automated Glaucoma Detection Using Variational Mode Decomposition from Fundus Images
DOI:
https://doi.org/10.37506/ijphrd.v11i6.9954Keywords:
Glaucoma, pre-processing, variational mode decomposition, feature extraction and normalization, singular value decomposition, support vector machine.Abstract
Glaucoma is a chronic eye disorder and one of the major causes of vision loss. Increased intraocular pressure
damaged the optic nurves and hence blindness. Available methods on glaucoma image classification are
expensive and slow. Therefore fast and low cost methods are needed. In this paper, glaucoma image
classification using two dimensional variational mode decomposition and support vector machine from
fundus images is proposed. The variational mode decomposition is used to decompose the glaucoma and
normal images. Features are extracted from decomposed sub band images. Selected and reduced features
are used to classify images in glaucoma or normal by support vector machine. The obtained accuracy,
sensitivity, specificity are 94.17 %, 95 %, and 95 %, respectively for tenfold cross validation technique.
Obtained results confirm that proposed method is adequate and improved over the state-of-the-art methods.
