TY - JOUR
T1 - Retinal vessel segmentation using a multi-scale medialness function
AU - Moghimirad, Elahe
AU - Hamid Rezatofighi, Seyed
AU - Soltanian-Zadeh, Hamid
PY - 2012/1
Y1 - 2012/1
N2 - Recently, automated segmentation of retinal vessels in optic fundus images has been an important focus of much research. In this paper, we propose a multi-scale method to segment retinal vessels based on a weighted two-dimensional (2D) medialness function. The results of the medialness function are first multiplied by the eigenvalues of the Hessian matrix. Next, centerlines of vessels are extracted using noise reduction and reconnection procedures. Finally, vessel radii are estimated and retinal vessels are segmented. The proposed method is evaluated and compared with several recent methods using images from the DRIVE and STARE databases.
AB - Recently, automated segmentation of retinal vessels in optic fundus images has been an important focus of much research. In this paper, we propose a multi-scale method to segment retinal vessels based on a weighted two-dimensional (2D) medialness function. The results of the medialness function are first multiplied by the eigenvalues of the Hessian matrix. Next, centerlines of vessels are extracted using noise reduction and reconnection procedures. Finally, vessel radii are estimated and retinal vessels are segmented. The proposed method is evaluated and compared with several recent methods using images from the DRIVE and STARE databases.
KW - Hessian matrix
KW - Medialness function
KW - Radius estimation
KW - Reconnection
KW - Retinal vessel segmentation
UR - http://www.scopus.com/inward/record.url?scp=83855162731&partnerID=8YFLogxK
U2 - 10.1016/j.compbiomed.2011.10.008
DO - 10.1016/j.compbiomed.2011.10.008
M3 - Article
SN - 0010-4825
VL - 42
SP - 50
EP - 60
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
IS - 1
ER -