ABSTRACT
In this Chapter the experimental results of the proposed glaucoma detection system discussed and their evaluations are explained in three sections: The first section for preprocessing and segmentation, the second section for glaucoma detection and the third section for glaucoma classification. The key characteristic features used in the research system is color, shape and texture features are the quantitative measure for diagnosing glaucoma. The quantitative evaluation of the proposed system is performed using the computed features compared with the gold standard database diagnosing based on standard parameters discussed in section (2.14). The performance of the OC and OD segmentation is analyzed using the thresholding algorithm. A comparison is also made using the Ensembles and SVM classification approaches. The performance of the glaucoma classification system is evaluated using receiver operating characteristic and area under ROC curve (AUC) for it is the diagnostic ability of binary classifier.
