Discriminative dictionary learning for retinal vessel segmentation using fusion of multiple features

Abstract

In recent years, automated retinal vessel segmentation has become especially essential for the early detection of some ophthalmological and cardiovascular diseases. In this paper, we have presented a new retinal vessel segmentation method via discriminative dictionary learning using fusion of multiple features, which is able to capture both thick and thin vessel structures. In the training stage, we employ six different enhancement algorithms to obtain multiple complementary features that contain rich vascular information. Then, the manually annotated ground-truth vessels are classified into thick or thin vessels as the label information, and the label consistent KSVD based framework is applied to train the dictionary for vessel segmentation. In the testing stage, comprehensive experiments are conducted on three datasets to measure segmentation performance with eight representative evaluation metrics. The average sensitivity reaches 0.7915, 0.7560 and 0.7202 respectively, suggesting that our method can segment tiny vascular structures well.