Retinal vessel segmentation by probing adaptive to lighting variations
This work addresses the problem of accurate vessel segmentation in medical imaging for healthcare applications, but it is incremental as it builds upon existing methods with a focus on lighting adaptation.
The paper tackles retinal vessel segmentation in fundus images by introducing a method adaptive to lighting variations, achieving an accuracy of 0.9454 on the DRIVE database, which is competitive with some state-of-the-art methods and outperforms others in low-contrast conditions.
We introduce a novel method to extract the vessels in eye fun-dus 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.