Automatic detection of peripapillary atrophy in retinal fundus images using statistical features

The presence of peripapillary atrophy (PPA) is associated with two kinds of diseases, namely glaucoma and myopia. PPA is one of the characteristics of these diseases that can be observed through retinal fundus images. We propose an automatic detection method of PPA in retinal fundus images using statistical features 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 may occur in each sector. There are three features used in this method namely, standard deviation, smoothness and third moment; they are selected using gain ratio method. The performance of the proposed method achieves the accuracy of 0.95, 0.96, and 0.96 for three different datasets. These are obtained using 155 retinal fundus images, from which training and testing data of 47 images and 108 images, respectively, are randomly selected.