Retinal vessel extraction by matched filter with first-order derivative of Gaussian

Bob Zhang, Lin Zhang, Lei Zhang, Fakhri Karray

Research output: Journal article publicationJournal articleAcademic researchpeer-review

462 Citations (Scopus)


Accurate extraction of retinal blood vessels is an important task in computer aided diagnosis of retinopathy. The matched filter (MF) is a simple yet effective method for vessel extraction. However, a MF will respond not only to vessels but also to non-vessel edges. This will lead to frequent false vessel detection. In this paper we propose a novel extension of the MF approach, namely the MF-FDOG, to detect retinal blood vessels. The proposed MF-FDOG is composed of the original MF, which is a zero-mean Gaussian function, and the first-order derivative of Gaussian (FDOG). The vessels are detected by thresholding the retinal image's response to the MF, while the threshold is adjusted by the image's response to the FDOG. The proposed MF-FDOG method is very simple; however, it reduces significantly the false detections produced by the original MF and detects many fine vessels that are missed by the MF. It achieves competitive vessel detection results as compared with those state-of-the-art schemes but with much lower complexity. In addition, it performs well at extracting vessels from pathological retinal images.
Original languageEnglish
Pages (from-to)438-445
Number of pages8
JournalComputers in Biology and Medicine
Issue number4
Publication statusPublished - 1 Apr 2010


  • Line detection
  • Matched filter
  • Retinal image segmentation
  • Vessel detection

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics


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