Automated glaucoma detection using GIST and pyramid histogram of oriented gradients (PHOG) descriptors

Effective diagnosis of glaucoma mainly relies on the analysis of optic disc characteristics of retina. Glaucoma is considered as second leading cause of blindness and its early detection prevents patients from temporary or permanent blindness. It effects the intensity and shape near optic disc of the retina. Fundus photography has revolutionized the field of ophthalmology and helped in visualizing the structure of optic disc. The proposed work aims to develop an automated diagnostic system based on fundus images for glaucoma disease. It focuses on extraction of GIST and pyramid histogram of oriented gradients (PHOG) features from preprocessed fundus images. The extracted features are ranked and selected through principal component analysis (PCA) to choose significant features. The classification into glaucomatous images is done with SVM classifier on fundus images of Drishti-GS1 and HRF databases. The results obtained from the proposed method are compared with recent glaucoma detection techniques in the literature, including deep learning methodologies, on the basis of accuracy and AUC parameters. The performance of the system is also validated by glaucoma expert from Sharp Sight Group of Eye Hospitals, Delhi-NCR, India. (C) 2019 Elsevier B.V. All rights reserved.