Automated Diagnosis of Glaucoma using Haralick Texture Features

Abstract

Glaucoma is the second leading cause of blindness worldwide. It is a disease in which fluid pressure in the eye increases continuously, damaging the optic nerve and causing vision loss. Computational decision support systems for the early detection of glaucoma can help prevent this complication. The retinal optic nerve fibre layer can be assessed using optical coherence tomography, scanning laser polarimetry, and Heidelberg retina tomography scanning methods. In this paper, we present a novel method for glaucoma detection using an Haralick Texture Features from digital fundus images. K Nearest Neighbors (KNN) classifiers are used to perform supervised classification. Our results demonstrate that the Haralick Texture Features has Database and classification parts, in Database the image has been loaded and Gray Level Cooccurrence Matrix (GLCM) and thirteen haralick features are combined to extract the image features, performs better than the other classifiers and correctly identifies the glaucoma images with an accuracy of more than 98%. The impact of training and testing is also studied to improve results. Our proposed novel features are clinically significant and can be used to detect glaucoma accurately.