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Classification of Glaucoma Fundus Images Using Curvelet Empirical Wavelet Transform

Glaucoma is a visual issue caused because of the expanded fluid pressure in the optic nerve. It harms the fibers of the nerves and leads to loss of the vision of eye. To overcome this problem, there is a need for developing a low computational complex, efficient, less expensive and non-invasive system. The aim of this paper is to propose a Computer Aided Design system to classify normal eye with the glaucoma eye fundus image using Curvelet Empirical Wavelet Transform (EWT) and support vector machine (SVM). Initially, images are preprocessed by extracting two dimensional gray, green, red, and blue channels from the retinal fundus images. Curvelet EWT segments, frequency spectrum and Correntropy features were assessed. The features were selected on the basis of the statistical analysis. SVM with a Gaussian kernel function is used to create the classifier model. The implementation of the Curvelet EWT has been compared with other state of art technique and results in 97% cross validation accuracy which is better among all other techniques.