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

Zhenlin Guo, Ping Lin, Guangrong Ji, Yangfan Wang

    Research output: Contribution to journalArticle

    5 Citations (Scopus)

    Abstract

    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).
    Original languageEnglish
    Pages (from-to)459-473
    Number of pages15
    JournalInverse Problems and Imaging
    Volume8
    Issue number2
    DOIs
    Publication statusPublished - May 2014

    Fingerprint Dive into the research topics of 'Retinal vessel segmentation using a finite element based binary level set method'. Together they form a unique fingerprint.

  • Cite this