TY - JOUR
T1 - Retinal vessel segmentation using a finite element based binary level set method
AU - Guo, Zhenlin
AU - Lin, Ping
AU - Ji, Guangrong
AU - Wang, Yangfan
PY - 2014/5
Y1 - 2014/5
N2 - In this paper we combine a few techniques to label blood vessels in the matched filter (MF) response image by using a finite element based binary level set method. An operator-splitting method is applied to numerically solve the Euler-Lagrange equation from minimizing an energy functional. Unlike the traditional MF methods, where a threshold is difficult to be selected, our method can automatically get more precise blood vessel segmentation using an enhanced edge information. In order to demonstrate the good performance, we compare our method with a few other methods when they are applied to a publicly available standard database of coloured images (with manual segmentations available too).
AB - In this paper we combine a few techniques to label blood vessels in the matched filter (MF) response image by using a finite element based binary level set method. An operator-splitting method is applied to numerically solve the Euler-Lagrange equation from minimizing an energy functional. Unlike the traditional MF methods, where a threshold is difficult to be selected, our method can automatically get more precise blood vessel segmentation using an enhanced edge information. In order to demonstrate the good performance, we compare our method with a few other methods when they are applied to a publicly available standard database of coloured images (with manual segmentations available too).
UR - http://www.scopus.com/inward/record.url?scp=84900421146&partnerID=8YFLogxK
U2 - 10.3934/ipi.2014.8.459
DO - 10.3934/ipi.2014.8.459
M3 - Article
AN - SCOPUS:84900421146
SN - 1930-8337
VL - 8
SP - 459
EP - 473
JO - Inverse Problems and Imaging
JF - Inverse Problems and Imaging
IS - 2
ER -