Retinal vessel segmentation using a finite element based binary level set method
Author(s) -
Zhenlin Guo,
Ping Lin,
Guangrong Ji,
Yangfan Wang
Publication year - 2014
Publication title -
inverse problems and imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.755
H-Index - 40
eISSN - 1930-8345
pISSN - 1930-8337
DOI - 10.3934/ipi.2014.8.459
Subject(s) - computer science , segmentation , finite element method , operator (biology) , level set (data structures) , filter (signal processing) , set (abstract data type) , binary number , level set method , energy functional , image segmentation , artificial intelligence , algorithm , computer vision , mathematics , mathematical analysis , arithmetic , biochemistry , chemistry , physics , repressor , transcription factor , gene , thermodynamics , programming language
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).
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