A self-adaptive matched filter for retinal blood vessel detection

Retinal fundus images are widely studied in medicine for the detection of certain pathologies such as diabetes and glaucoma, the two major reasons for blindness. In this paper, a self-adaptive matched filter for the detection of blood vessels in the retinal fundus images is proposed. In particular, a novel synergistic combination of the vesselness filter with high sensitivity and the matched filter with high specificity is obtained using orientation histogram. Experiments on the publicly available DRIVE database clearly show that the proposed strategy outperforms several existing methods. Comparable performance with some of the state-of-the-art methods has also been obtained on the STARE and CHASE databases.