Feature Extraction using Contourlet Transform for Glaucomatous Image Classification

is one of the leading causes of blindness. It is caused by increased Intra Ocular Pressure (IOP) due to the malfunction of the drainage structure of the eyes. There are several methods to detect the Glaucoma from a human eye in the initial stages. In this paper a new system is proposed, which automatically detect Glaucoma disease in the human eye from the fundus database images. The feature extraction within images is based on Contourlet Transform (CT) and the classification is based on Support Vector Machine (SVM).In the existing system Discrete Wavelet Transform is used for feature extraction of Glaucoma images. Discrete Wavelet Transform have less number of information and the features obtained are only in three directions(vertical, horizontal, diagonal).Hence for better resolution and more directions Contourlet Transform is used. The better classification is arrived by extracting and selecting the best features from the contourlet coefficients of the fundus image and the outputs are used as an input to the Support Vector Machine classifier for classification with high accuracy. The proposed system classifies the input fundus images as normal or abnormal with very high accuracy.