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Retinal Vessel Extraction Using First-Order Derivative of Gaussian and Morphological Processing

  • M. M. Fraz
  • P. Remagnino
  • A. Hoppe
  • B. Uyyanonvara
  • Christopher G. Owen
  • Alicja R. Rudnicka
  • S. A. Barman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

The change in morphology, diameter, branching pattern and/or tortuosity of retinal blood vessels is an important indicator of various clinical disorders of the eye and the body. This paper reports an automated method for segmentation of blood vessels in retinal images by means of a unique combination of differential filtering and morphological processing. The centerlines are extracted by the application of first order derivative of Gaussian in four orientations and then the evaluation of derivative signs and average derivative values is made. The shape and orientation map of the blood vessel is obtained by applying a multidirectional morphological top-hat operator followed by bit plane slicing of a vessel enhanced grayscale image. The centerlines are combined with these maps to obtain the segmented vessel tree. The approach is tested on two publicly available databases and results show that the proposed algorithm can obtain robust and accurate vessel tracings with a performance comparable to other leading systems.

Keywords

Positive Predictive Value Retinal Vessel Matthews Correlation Coefficient Active Contour Model Fundus Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. M. Fraz
    • 1
  • P. Remagnino
    • 1
  • A. Hoppe
    • 1
  • B. Uyyanonvara
    • 2
  • Christopher G. Owen
    • 3
  • Alicja R. Rudnicka
    • 3
  • S. A. Barman
    • 1
  1. 1.Faculty of Computing, Information Systems and MathematicsKingston UniversityLondonUnited Kingdom
  2. 2.Department of Information TechnologyThammasat UniversityThailand
  3. 3.Division of Population Health Sciences and Education, St. George’sUniversity of LondonUnited Kingdom

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