Author : Saravanan M 1
Date of Publication :23rd March 2018
Abstract: Glaucoma, the most common cause of blindness is the disease of the optic nerve of the eye and can lead to ultimate blindness if not treated at an early stage. Raised intraocular pressure, increase in cup to disk ratio and visual field test are some of the measures for such a disease. The main objective of this paper is to find an automated tool to detect glaucoma at an early stage and to classify this disease based on its severity and damage of the optic fiber. The objective of this study is pre-processing of retinal fundus image for enhancing the quality which is required for further processing and to design a novel algorithm to measure the cup to disc ratio of retinal fundus image from the online database and classify the disease according to its severity using fuzzy classification toolbox in MATLAB. This paper presents Evaluation K-mean and Fuzzy c-mean image segmentation based Clustering classifier. It was followed by thresholding and level set segmentation stages to provide accurate region segment. The performance and evaluation of the given image segmentation approach were evaluated by comparing K-mean and Fuzzy c-mean algorithms in case of accuracy, processing time, Clustering classifier, and Features and accurate performance results. The database consists Glaucoma affected images executed by K-mean and Fuzzy c-mean image segmentation based Clustering classifier. The experimental results confirm the effectiveness of the proposed Fuzzy c-mean image segmentation based Clustering classifier. The statistical significance Measures of mean values of Peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE) and discrepancy are used for Performance Evaluation of K-mean and Fuzzy c-mean image segmentation. The algorithm’s higher accuracy can be found by the increasing number of classified clusters and with Fuzzy c-mean image segmentation.
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