Robust retinal blood vessel segmentation using a patch-based statistical adaptive multi-scale line detector

Shahzaib Iqbal*, Khuram Naveed, Syed S. Naqvi, Asim Naveed, Tariq M. Khan

*Corresponding author for this work

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review


The diagnosis of diseases using the retinal vasculature has long been a focus of medical research. Segmenting the vessels from retinal images is still a difficult task because of the variability in retinal vascular thickness and the inhomogeneity of image intensity. In this research work, a two-stage retinal vessel segmentation method is proposed. At the first stage, the large vessels are segmented by using multi-scale line detection by suppressing the small vessels through non-local mean filtering. At the second stage, the image is enhanced through noise suppression attributed largely to the block-matching 3-D filtering inside the nonsubsampled contourlet transform. Then a patch-based multi-scale line detector with adaptive statistical thresholding is used. Finally, both large and small vessels are combined to reconstruct the final vessel segmented image. The proposed method is evaluated on two publicly available databases and achieves an average sensitivity of 82.61%, and 81.67% on the DRIVE and STARE datasets respectively. The proposed method outperforms other state-of-the-art methods and has proven to be effective in boosting the sensitivity of retinal vessel segmentation.

Original languageEnglish
Article number104075
JournalDigital Signal Processing: A Review Journal
Publication statusPublished - Jul 2023


  • Digital Image processing
  • Multiscale line detector
  • Retinal vessel extraction
  • Statistical adaptive thresholding


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