Automated Glaucoma Detection Using Support Vector Machine Classification Method

Glaucoma is an eye disease that can result in blindness if it is not detected and treated in proper time. Increased intraocular pressure (IOP) of the fluid in the eye often causes glaucoma. Glaucoma is the second leading cause of blindness in the world and is called as the “Silent Thief of Sight”. Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) techniques for detecting glaucoma is very expensive. A method to diagnose glaucoma using digital fundus images is presented in this paper. The aim of our proposed method is to apply image processing techniques on the digital fundus images of the eye for analysing glaucomatous eye and normal eye. Images pre-processing techniques such as noise removal and contrast enhancement, Principal Component Analysis (PCA) method for feature extraction and Support Vector Machine (SVM) method for image classification are used in the proposed method. All these techniques are implemented via MATLAB which provides variety of options for image processing that enable us to extract the required features and information from the images.