Towards automated diagnostic evaluation of retina images

In this paper we describe the automatic segmentation of the optic nerve head (ONH) with the long-term goal of automatically diagnosing early stages of glaucoma. The images are average images obtained from a scanning laser ophthalmoscope (SLO). The segmentation consists of the main s teps of finding a region of interest containing the ONH, constraining the search space for final segmentation, and computing the fine segmentation by an active contour model. The agreement of “true positive pixels,” i.e., pixels attributed to the ONH by both manual and automatic segmentation, is very good. The classification results from three different classifiers using manual or automatic segmentation still show an advantage of manual segmentation. One means to further improve the automatic segmentation is to use information from an SLO as well as from a fundus camera.