Computationally Efficient Optic Nerve Head Detection in Retinal Fundus Images

TL;DR

This paper introduces a fast, robust method for detecting the optic nerve head in retinal images using Radon transform and multi-overlapping windows, outperforming existing algorithms in speed while maintaining high accuracy.

Contribution

The paper presents a novel, computationally efficient detection method combining Radon transform and multi-overlapping windows for retinal images.

Findings

High detection rates across three databases

Faster processing compared to existing methods

Robust detection despite image variations

Abstract

This paper presents a computationally efficient method for the detection of optic nerve head in both color fundus and fluorescein angiography images. It involves a combination of Radon transformation and multi-overlapping windows within an optimization framework in order to achieve a robust detection in the presence of various structural, color, and intensity variations in such images. Three databases have been examined and it is shown that the introduced method provides high detection rates while achieving faster proceeding rates than the existing algorithms that possess comparable detection rates.