Retinal vessel segmentation using a finite element based binary level set method

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 - https://www.scopus.com/pages/publications/84900421146

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 -