Decision support system for glaucoma diagnosis using optical coherence tomography images

Abstract

Glaucoma is an irreversible disease that damages eye's optic nerve. To avoid the vision loss, early diagnosis of glaucoma is required. In this work, an efficient Decision Support System (DSS) for glaucoma diagnosis based on Tetrolet Transform (TT) is proposed. Optical Coherence Tomography (OCT) images are used for the diagnosis. The classification of glaucomatous images is consists of three different stages; preprocessing, feature extraction and classification stage. In the preprocessing stage, Region of Inertest (ROI) is extracted from the OCT image that contains only the retinal area. In feature extraction stage, the ROI image is decomposed by TT at predefined resolution levels and statistical features are extracted from both TT decomposed image and Gray Level Difference Method (GLDM) of TT decomposed image for classification. Finally, a decision is made using Support Vector Machine (SVM) classifier. Results show that the DSS for glaucoma diagnosis provides an accuracy of 99.5% with 100% sensitivity and 99% specificity at 3rd level TT features.