Blood vessel segmentation methodologies in retinal images - A survey

  • Authors:

  • M. M. Fraz;P. Remagnino;A. Hoppe;B. Uyyanonvara;A. R. Rudnicka;C. G. Owen;S. A. Barman

  • Affiliations:

  • Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University London, London, United Kingdom;Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University London, London, United Kingdom;Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University London, London, United Kingdom;Department of Information Technology, Sirindhorn International Institute of Technology, Thammasat University, Bangkok, Thailand;Division of Population Health Sciences and Education, St. George's, University of London, London, United Kingdom;Division of Population Health Sciences and Education, St. George's, University of London, London, United Kingdom;Digital Imaging Research Centre, Faculty of Science, Engineering and Computing, Kingston University London, London, United Kingdom

  • Venue:
  • Computer Methods and Programs in Biomedicine
  • Year:
  • 2012

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Abstract

Retinal vessel segmentation algorithms are a fundamental component of automatic retinal disease screening systems. This work examines the blood vessel segmentation methodologies in two dimensional retinal images acquired from a fundus camera and a survey of techniques is presented. The aim of this paper is to review, analyze and categorize the retinal vessel extraction algorithms, techniques and methodologies, giving a brief description, highlighting the key points and the performance measures. We intend to give the reader a framework for the existing research; to introduce the range of retinal vessel segmentation algorithms; to discuss the current trends and future directions and summarize the open problems. The performance of algorithms is compared and analyzed on two publicly available databases (DRIVE and STARE) of retinal images using a number of measures which include accuracy, true positive rate, false positive rate, sensitivity, specificity and area under receiver operating characteristic (ROC) curve.