Retinal vessels segmentation using supervised classifiers decisions fusion

Ophthalmology is a significant branch of the biomedical field which requires computer-aided automated techniques for pathology identification. Within this framework, an important concern is the accurate segmentation of the retinal blood vessels. A reference approach in the literature to this task consists in the classification of the pixels as vessels or non-vessels, using as discriminative features the green channel intensity, two-dimensional Gabor wavelet responses and some variants of LBP descriptors. However the discriminative power of this feature set is not always sufficient to provide a really highly accurate segmentation. In this paper we propose a new approach, combining powerful machine learning classifiers: support vector machines and neural networks over the same feature set, to improve the classification accuracy by a weighted decision fusion. The experimental results obtained on the DRIVE database show that the segmentation accuracy is increased up to 94%, which is superior to similar segmentation methods from the literature using neural networks, Bayesian, unsupervised classifiers and even support vector machines individually. When these results are further combined with the output of matched filters applied on the retinal images, the segmentation accuracy is further increased, by a better identification of the fine vessels.