Automatic Extraction of the Optic Disc Boundary for Detecting Retinal Diseases

In this paper, we propose an algorithm based on active shape model for the extraction of Optic Disc boundary. The determination of Optic Disc boundary is fundamental to the automation of retinal eye disease diagnosis because the Optic Disc Center is typically used as a reference point to locate other retinal structures, and any structural change in Optic Disc, whether textural or geometrical, can be used to determine the occurrence of retinal diseases such as Glaucoma. The algorithm is based on determining a model for the Optic Disc boundary by learning patterns of variability from a training set of annotated Optic Discs. The model can be deformed so as to reflect the boundary of Optic Disc in any feasible shape. The algorithm provides some initial steps towards automation of the diagnostic process for retinal eye disease in order that more patients can be screened with consistent diagnoses. The overall accuracy of the algorithm was 92% on a set of 110 images.