Conference Paper2009Cited by 3

Phase congruency based retinal vessel segmentation

M Ashraful Amin, Hong Yan

2009 International Conference on Machine Learning and Cybernetics

IEEE, Vol. 4, pp. 2458–2462

  • Good Health and Well-being — Relevance: 87%
DOI: 10.1109/icmlc.2009.5212201URL

Abstract

Detection of blood vessels in a retinal fundus image is the preliminary step to diagnose several retinal diseases. There exist a number of methods to accomplish this task automatically. However, all of these methods suffer from lengthy processing time. The other major use of retina scanning is biometric authentication, for which a real time vessel detection system is required. In this work we describe a method that acquires binary vessel image from a color retinal fundus image in near real time. This method first generates the phase congruency image of the green channel of a color retinal image, and then thresholding is applied on the phase congruency image to obtain the blood vessels. Our method is able to acquire the blood vessels from a retinal fundus image within 10 seconds on a PC with the values of ‘accuracy’ and ‘area under ROC’: (0.92, 0.94) for the standard testing database called DRIVE. However, a (0.90, 0.91) accuracy and area under ROC can be acquired in less than 2 sec.

Keywords

  • blood vessel detection
  • fundus images
  • Phase congruency
  • retinal vessel segmentation
  • Thresholding
  • DRIVE dataset
  • biometric authentication
  • real-time processing
  • green channel

CCDS Authors

Prof. M Ashraful Amin, PhD
M Ashraful Amin, PhD

Prof. M Ashraful Amin, PhD

  • Founder Director, CCDS
  • Director, AI/ML, HCI & DS Wings

Professor

+1 more affiliation

Department of Information Sciences and Technology

George Mason University, USA

Published at

ACL, CHI, UbiComp, ECAI, WACV, PAKDD, IJCNN, ICPR, ICIP, HCII, CLEF, BLP, Scientific Reports, JMIR, IEEE Access, Smart Agricultural Technology, Frontiers in Computational Neuroscience, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Machine Learning, Cognitive & Vision Science, Cybernetics, Surveillance & Security, ICT in Education, Health, & Agriculture, Human-Computer Interaction, Internet of Things, Robotics

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Cited By

  1. 1.Simona Crihalmeanu, Arun Ross. (2011). Multispectral scleral patterns for ocular biometric recognition. Pattern Recognition Letters, 33(14), 1860–1869[10.1016/j.patrec.2011.11.006]
  2. 2.Yalin Zheng. (2013). Retinal blood vessel detection using multiscale line filter and phase congruency.
  3. 3.B.H. Shekar, Smitha M.L.. (2015). Phase congruency and morphology based approach for text localization in videos. , 1, 2221–2225[10.1109/icacci.2015.7275947]
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