An approach to localize the retinal blood vessels using bit planes and centerline detection

  • Authors:

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

  • Affiliations:

  • Digital Imaging Research Centre, Faculty of Science and Engineering, Kingston University London, London, United Kingdom;Digital Imaging Research Centre, Faculty of Science and Engineering, Kingston University London, London, United Kingdom;Digital Imaging Research Centre, Faculty of Science and Engineering, Kingston University London, London, United Kingdom;Digital Imaging Research Centre, Faculty of Science and Engineering, Kingston University London, London, United Kingdom;TPPD, Pakistan Institute of Nuclear Science and Technology (PINSTECH), Nilore, Islamabad, Pakistan;Department of Information Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani, 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

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

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Abstract

The change in morphology, diameter, branching pattern 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. A unique combination of techniques for vessel centerlines detection and morphological bit plane slicing is presented to extract the blood vessel tree from the retinal images. The centerlines are extracted by using the first order derivative of a Gaussian filter in four orientations and then evaluation of derivative signs and average derivative values is performed. Mathematical morphology has emerged as a proficient technique for quantifying the blood vessels in the retina. The shape and orientation map of blood vessels is obtained by applying a multidirectional morphological top-hat operator with a linear structuring element followed by bit plane slicing of the vessel enhanced grayscale image. The centerlines are combined with these maps to obtain the segmented vessel tree. The methodology is tested on three publicly available databases DRIVE, STARE and MESSIDOR. The results demonstrate that the performance of the proposed algorithm is comparable with state of the art techniques in terms of accuracy, sensitivity and specificity.