OD Localization Using Rotational 2D Vessel Projection with Decision Tree Classification

Abstract

Automatic Optic Disc (OD) localization is an important problem in ophthalmic image processing. Knowing its location helps doctors with the early detection of preventable eye diseases. Inspired by a fast and accurate OD localization algorithm utilizing the vessel projection technique that is usually inefficient when the OD in the image is unusually pale, we employed the decision tree with 5 features to improve the accuracy of the existing algorithm. Also to overcome the problem of poor accuracy when the image is tilted, we repeatedly run this improved algorithm on a series of images tilted at different degree from the original image to obtain the voted location of the OD. The proposed method has been tested on different starting angles between 0 to 180 degrees from Structured Analysis of the Retina (STARE) and retinopathy of prematurity (ROP) datasets. We achieve an average accuracy of up to 86% with an average computation time per image of only 13 seconds per image. Our approach outperforms two other based approaches, Mahfouz and Rotational 2D Vessel Projection (RVP), by up to 34% and 12%, respectively.