Optical disk segmentation in human retina images with golden eagle optimizer

Abstract

In ophthalmology, examination of the optic nerve and disc of the retina is a common way to diagnose a variety of eye diseases such as aging, diabetes, optic neuritis, glaucoma, papilledema, and ischemic optic neuropathy. This study presents the implementation of three combined methods based on the Golden Eagle algorithm, backup vector machine and geometrically active contour method and image segmentation. These methods have been tested using the well-known retina database benchmark, RIM-ONE. This study uses preprocessing based on the geometrically active Shannon contour guided by the Golden Eagle algorithm and post-processing based on regular spaced cross-section segmentation. The Golden Eagle method is more efficient than approaches such as the geometrically active contour and the SVM method. The proposed method, compared to other methods, shows higher mean values in terms of image similarity and statistical index. It is confirmed that the proposed Golden Eagle method could be used in the future to assess the clinical fit of retinal images.