Automated glaucoma assessment from color fundus images using structural and texture features. (August 2022) - Nawaldgi, Sharanagouda; Lalitha, Y S
Highlights: A new model of automated glaucoma assessment from color fundus images (CFI) is proposed. Glaucoma assessment is based on structural and texture features. Evaluation is performed on two datasets consisting of 806 CFI images. Classification using Linear Discriminant Analysis (LDA), Random forest, Support Vector Machine (SVM) and Naïve Bayesian. Abstract: Glaucoma is an eye disease that damages optic nerves in eye leading to loss of vision. It is recognized to be the one of the major cause of blindness across the world. The detection of glaucoma in its early stage followed by treatment is the only way forward because damage done by the disease is irreversible. Hence there is a requirement of a large scale glaucoma screening. Manual screening of glaucoma at large scale is quite a challenging task due to lack of skilled manpower in ophthalmology. To address the above issue, many works have been proposed towards automating glaucoma detection suitable for large scale screening. On similar line, we in this proposed work present a novel method for automated glaucoma assessment from color fundus images (CFI) using structural features and texture features. Structural features such as cup to disk ratio (CDR), rim to disc ratio (RDR) and texture features extracted through the Grey-Level Co occurrence Matrix (GLCM) and entropy of images, histogram of gradient (HOG) features are used for glaucoma assessment. The classifiers used are Linear Discriminant Analysis (LDA), Random forest, Support Vector Machine (SVM) and Naïve Bayesian. The results obtained by the proposed work yield an overall efficiency of over 89%.