TY - JOUR
T1 - Supervised vessel delineation in retinal fundus images with the automatic selection of B-COSFIRE filters
AU - Strisciuglio, Nicola
AU - Azzopardi, George
AU - Vento, Mario
AU - Petkov, Nicolai
PY - 2016/6/27
Y1 - 2016/6/27
N2 - The inspection of retinal fundus images allows medical doctors to diagnose various pathologies. Computer-aided diagnosis systems can be used to assist in this process. As a first step, such systems delineate the vessel tree from the background. We propose a method for the delineation of blood vessels in retinal images that is effective for vessels of different thickness. In the proposed method, we employ a set of B-COSFIRE filters selective for vessels and vesselendings. Such a set is determined in an automatic selection process and can adapt to different applications. We compare the performance of different selection methods based upon machine learning and information theory. The results that we achieve by performing experiments on two public benchmark data sets, namely DRIVE and STARE, demonstrate the effectiveness of the proposed approach.
AB - The inspection of retinal fundus images allows medical doctors to diagnose various pathologies. Computer-aided diagnosis systems can be used to assist in this process. As a first step, such systems delineate the vessel tree from the background. We propose a method for the delineation of blood vessels in retinal images that is effective for vessels of different thickness. In the proposed method, we employ a set of B-COSFIRE filters selective for vessels and vesselendings. Such a set is determined in an automatic selection process and can adapt to different applications. We compare the performance of different selection methods based upon machine learning and information theory. The results that we achieve by performing experiments on two public benchmark data sets, namely DRIVE and STARE, demonstrate the effectiveness of the proposed approach.
U2 - 10.1007/s00138-016-0781-7
DO - 10.1007/s00138-016-0781-7
M3 - Article
VL - 27
SP - 1137
JO - Machine Vision and Applications
JF - Machine Vision and Applications
SN - 0932-8092
IS - 8
ER -