Comparison with Observer Appraisals of Fundus Images and Diagnosis by using Learning Vector Quantization

Abstract

This paper proposes a method for diagnosis of glaucoma by using Learning Vector Quantization(LVQ) technique from a fundus image. The diagnosis accuracy was determined by using the proposed method from the data set which consists of four components. They are two size components and two threshold ones in the excavation of the optic disk and the optic disk. In the experiments, we have achieved a maximum accuracy rate of 78.9%, which is comparable to the results by the observer appraisals of the fundus images.