Vessel segmentation in eye fundus images using ensemble learning and curve fitting

A novel segmentation algorithm for the detection of retinal vessels in funduscopic images is proposed, in which the benefits of both supervised and unsupervised methods are exploited. Ensemble learning based segmentation (ELBS) is employed for the segmentation of large and medium sized vessels, after which a local curve fitting technique is used for the detection of the thin retinal vessels. The general ELBS algorithm is modified to boost performance by the incorporation of specific knowledge of false negative segmentation result areas. Curve fitting is based on a two-hypotheses polynomial regression and is capable of automatically removing outliers from a point cloud. Evaluation on the DRIVE database compared the presented method favorably to previously published algorithms. Sensitivity and specificity were 0.8854 and 0.9363.