Papilledema Detection in Fundus Images Using Hybrid Feature Set

Abstract

Papilledema is the swelling of the optic disc. It is a symptom of several diseases including some life-threatening diseases as well. Therefore it needs to be detected as soon as possible. If it is not detected on time and reaches its final stage, it can also lead to permanent vision loss. In this paper, we have given a simple and efficient method to automatically detect papilledema through fundus images. The dataset used is taken from STARE. After preprocessing of the fundus images, 13 features are extracted which are helpful in detecting this disease and combined to form a feature set. After feature extraction, classification is done using SVM classifier on this feature set. The method gives an accuracy of 96.67%.