A probabilistic based method for tracking vessels in retinal images

Vessel detection is an important process in many medical imaging applications. In this paper, an edge tracking scheme is proposed for the detection of blood vessels in retinal images. This method detects edge points iteratively based on a Bayesian approach using local grey levels statistics and continuity properties of blood vessels. Combining the grey level profile and vessel geometric properties improves the accuracy and robustness of the tracking process. Experiments on both synthetic and real retinal images show promising results.