A MODIFIED SPATIAL FUZZY C-MEANS CLUSTERING ALGORITHM FOR DETECTING GLAUCOMAIN RETINAL FUNDUS IMAGES

Abstract

Glaucoma is a disease which affects the eye and causes blindness. It is an ophthalmologist disease characterized by an increase in Intraocular Pressure (IOP). The glaucoma usually affects the optic disc on the retina which increases the cup size. There are various parameters to identify and diagnose glaucoma. The clustering technique is introduced to detect the glaucoma from the optic disc and cup in the retinal fundus images. Fuzzy C Means (FCM) Clustering is used for clustering the data in which the data points are clustered with different membership degree. But it does not fully utilize the spatial information in the image. The Modified Spatial Fuzzy C-Means clustering with spatial rotation has been proposed to detect glaucoma in retinal fundus images. The first and foremost step is preprocessing operation, in which the optic cup and disk of the input image is being rotated. Initially the optic disk is rotated in some angle and the distance between the data points are measured and a cluster is formed based on the centroid. The centroid and data point along with the cluster can be identified in each step then the common set of points is clustered together. This process continues until no more centroid is found. The cluster with more data points that do not match with the original image is considered as the retinal image with glaucoma disease. In future, this algorithm can be extended to larger clinical databases in order to identify the glaucoma at the maximum level.