A fractional filter based efficient algorithm for retinal blood vessel segmentation

Abstract

This paper presents a new fractional filter and an algorithm for retinal blood vessel segmentation. The proposed fractional filter is designed with the help of a weighted fractional derivative and an exponential weight factor. We have utilized the fractional filter and the eigenvalue maps of a local covariance matrix to develop the algorithm for retinal vessel segmentation. The local covariance matrix is formed by a second-order image moment. Experiments are performed on two well-studied evaluation databases named STARE and DRIVE. Experimental results show that the proposed method is computationally efficient, and the average accuracy of the vessel segmentation on STARE and DRIVE databases are 95.73% and 94.76%, respectively. The performance of the proposed method is compared with other existing methods. Simulation results show that the average performance of the proposed method is comparatively better than most of the discussed methods.