Comparative Evaluation of Contourlet and Wavelet Transform for Feature Extraction in Glaucoma Images

Background/Objectives: In this paper our proposed system easily detects the glaucoma affected eye from the fundus image database collected from the nearest eye hospital. Methods/Statistical Analysis: The feature extraction is done by contourlet transform and the best feature is selected for classification. Support vector machine is mainly applied for classification of images. Findings: In the conventional methods Wavelet Transform is applied for feature extraction of images and Support Vector Machine (SVM) adapted for classifying the Glaucoma images with non-affected and affected. The accuracy of classification is evaluated using existing and proposed techniques. Applications/Improvements: The proposed system is applied to find the Glaucoma disease of human eye with accurately which eliminates the human error to examine the disease of human eye. The system automatically finds the disease of human eye within the seconds from the applied fundus image.