Automatic Detection of Glaucoma Using Disc Optic Segmentation and Feature Extraction

Digital image processing is highlighted in the medical setting for automatic diagnosis of diseases. Glaucoma has no cure, and is the second leading cause of blindness worldwide. Currently there are treatments to prevent vision loss, but the disease will be discovered still in the early stages. Thus, this study aims to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study was: image acquisition, segmentation of the optic disc area (DO) in retinal images, color feature extraction and entropy in the targeted area and shortly after the selection of attributes. Finally, classification of images was conducted for identifying glaucoma.