On retinal blood vessel extraction using curvelet transform and differential evolution based maximum fuzzy entropy

This paper proposes a new method based on multiple thresholds for automatic extraction of blood vessels specially from a low contrast and non-uniformly illuminated background of retina. Curvelet transform is used to extract the finest details along the vessels since it can represent the lines, the edges and the curvatures very well. Next matched filtering is done to intensify the blood vessels´ response in the enhanced image. The multiple threshold values for the maximum matched filter response that maximize the fuzzy entropy are considered to be the optimal thresholds to extract the different types of vessel silhouettes from the background. Differential Evolution algorithm is used to specify the optimal combination of the fuzzy parameters. Performance is evaluated on publicly available DRIVE database and is compared with the existing blood vessel extraction methods. Simulation results demonstrate that the proposed method outperforms the existing methods in detecting the long and the thick as well as the short and the thin vessels.