Automatic retinal vessel segmentation using multi-scale superpixel chain tracking

The segmentation of retinal vessel and its structure information are important for computer-aided diagnosis and treatment of many diseases. This work proposes a superpixel-based chain tracking method for segmentation of retinal vessels. First, a multi-scale superpixel segmentation framework is developed to split the image into patches, which are utilized as the basic unit of the vessel-tracking procedure. Second, a vessel chain model which consists of a series of superpixel nodes is proposed for accurately segmenting small vessels. Third, vessel tracking is achieved by a two-stage procedure where vessel regions with good and bad imaging quality are handled differently. Finally, a maximum gradient method is proposed to estimate the vessel centerline and boundary. The proposed method was validated on synthetic data and public retinal image datasets. Experimental results demonstrate that the proposed method can accurately track the vascular skeletons, and the tracking accuracy can reach 0.9636.