Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters

Detection and tracking algorithms of the blood vessel network in the retinal images are proposed. Two main groups of algorithms are employed for this task, i.e., scanning and tracking. According to the known blood vessel feature, a second-order derivative Gaussian matched filter is designed and used to locate the center point and width of a vessel in its cross sectional profile. Together with this the extended Kalman filter is employed for the optimal linear estimation of the next possible location of blood vessel segment by appropriate formulation of its pattern changing process and observation model. To check the bifurcation in the vessel network, a simple branching detection strategy is implemented during tracking. The proposed algorithms all work well in the whole tracking process and can detect more complete vessel network in the ocular fundus photographs.