Shape and texture based identification of glaucoma from retinal fundus images

Among the eye disorders, glaucoma occurs due to the increase of intra-ocular pressure that causes irreversible damage of the optic nerve and leads to blindness. Therefore to avoid high cost machine usage, a novel method proposed for detecting glaucoma using shape and texture features. Initially, the retinal fundus images are decomposed using quasi bi-variate variational mode decomposition (QB-VMD) technique, the frequencies obtained from QB-VMD subjected to pyramid histogram oriented gradient (PHOG) and invariant Haralick texture features. The extracted combinational features are classified using combination of exponential polynomial support vector machines (EP-SVM) and bagged ensemble approach. The proposed method simulated on ACRIMA and Drishti-GS1 datasets using 10-fold cross validation and evaluated the performance metrics like accuracy, sensitivity, specificity and F-score. The simulation results and evaluation metrics show that the proposed approach achieves superior classification performance compared to other state-of-the-art approaches.