GLAUCOMA DIAGONSIS FCM_TK ALGORITHM BASED ON FUNDS CAMERA

Glaucoma is considered one of the most important human eye diseases that causes loss of vision. The main objective of this paper is extracting the features from fundus image to evaluate and minimize the risk of Glaucoma. This evaluation based on the Cup-to-Disc Ratio (CDR) of a color retinal fundus image. Actually, CDR is the actual value for the evolution of the risk of Glaucoma. Features used to evaluate the Glaucoma risk are extracted by detection and segmentation process. In this paper a method is proposed for Glaucoma image classification. The proposed method is as follows: the features extracted by discrete wavelet transform (DWT) and then followed by Teager-Kiaser Energy (TKE) operator to improve the detection quality, then classification is done based on fuzzy clustering method. The results showed that the proposed methods succeeded to diagnose the Glaucoma disease with high accuracy compared with the other state of the art techniques.