Retinal Vessel Segmentation by Probing Adaptive to Lighting Variations

We introduce a novel method to extract the vessels in eye fundus images which is adaptive to lighting variations. In the Logarithmic Image Processing framework, a 3-segment probe detects the vessels by probing the topographic surface of an image from below. A map of contrasts between the probe and the image allows to detect the vessels by a threshold. In a lowly contrasted image, results show that our method better extract the vessels than another state-of the-art method. In a highly contrasted image database (DRIVE) with a reference, ours has an accuracy of 0.9454 which is similar or better than three state-of-the-art methods and below three others. The three best methods have a higher accuracy than a manual segmentation by another expert. Importantly, our method automatically adapts to the lighting conditions of the image acquisition.

1. Introduction

Extracting vessels in eye fundus images has been explored in numerous papers, e.g. [1]–[6]. However, these methods may present limitations when there are strong lighting variations in images. The existence of screening programmes for diabetic retinopathy has led to the creations of large databases of eye fundus images which contain contrast variations. They can be due to: the inhomogeneous absorption of the eye or to different lighting conditions [7]. The aim of this paper is to introduce a vessel segmentation method which is adaptive to these lighting variations in colour eye fundus images. After having complemented the luminance of these images, the vessels appear as a positive relief (i.e. a “chain of mountains”) in the image topographic surfaces. Their detection is made by a probe composed of three parallel segments, where the central segment has a higher intensity than both others. When the probe is inside a vessel (i.e. a “mountain”), the intensity difference between its external segments and the bottom of the mountain is minimal, whereas when the probe is outside a vessel, the intensity difference becomes greater. This principle will be used to detect the vessels. The adaptivity to lighting variations is due to the Logarithmic Image Processing model [8]. Let us present our method, before showing some experiments and results.