Automatic detection of peripapillary atrophy in retinal fundus imagesusing statistical features

Abstract

tThe presence of peripapillary atrophy (PPA) is associated with two kinds of diseases, namely glaucomaand myopia. PPA is one of the characteristics of these diseases that can be observed through retinal fundusimages. We propose an automatic detection method of PPA in retinal fundus images using statistical fea-tures and Backpropagation Neural Network. In this research, those images are classified into two classes:no-PPA and PPA. The features are extracted from the focal areas, which capture the areas where PPA mayoccur in each sector. There are three features used in this method namely, standard deviation, smoothnessand third moment; they are selected using gain ratio method. The performance of the proposed methodachieves the accuracy of 0.95, 0.96, and 0.96 for three different datasets. These are obtained using 155retinal fundus images, from which training and testing data of 47 images and 108 images, respectively,are randomly selected.