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Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification

  • Muhammad Moazam Fraz
  • Alicja R. Rudnicka
  • Christopher G. Owen
  • Sarah A. Barman
Original Article

Abstract

Purpose

Automatic segmentation of the retinal vasculature is a first step in computer-assisted diagnosis and treatment planning. The extraction of retinal vessels in pediatric retinal images is challenging because of comparatively wide arterioles with a light streak running longitudinally along the vessel’s center, the central vessel reflex. A new method for automatic segmentation was developed and tested.

Method

   A supervised method for retinal vessel segmentation in the images of multi-ethnic school children was developed based on ensemble classifier of bootstrapped decision trees. A collection of dual Gaussian, second derivative of Gaussian and Gabor filters, along with the generalized multiscale line strength measure and morphological transformation is used to generate the feature vector. The feature vector encodes information to handle the normal vessels as well as the vessels with the central reflex. The methodology is evaluated on CHASE_DB1, a relatively new public retinal image database of multi-ethnic school children, which is a subset of retinal images from the Child Heart and Health Study in England (CHASE) dataset.

Results

   The segmented retinal images from the CHASE_DB1 database produced best case accuracy, sensitivity and specificity of 0.96, 0.74 and 0.98, respectively, and worst case measures of 0.94, 0.67 and 0.98, respectively.

Conclusion

   A new retinal blood vessel segmentation algorithm was developed and tested with a shared database. The observed accuracy, speed, robustness and simplicity suggest that the algorithm may be a suitable tool for automated retinal image analysis in large population-based studies.

Keywords

Medical imaging Retinal image analysis Ensemble classification Image segmentation  Boot strapped decision tree 

Notes

Conflict of interest

M. Moazam Fraz, Alicja R. Rudnicka, Christopher G. Owen and Sarah A. Barman declare that they have no conflict of interest.

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Copyright information

© CARS 2013

Authors and Affiliations

  • Muhammad Moazam Fraz
    • 1
  • Alicja R. Rudnicka
    • 2
  • Christopher G. Owen
    • 2
  • Sarah A. Barman
    • 1
  1. 1.School of Computing and Information Systems, Faculty of Science Engineering and ComputingKingston University LondonLondonUK
  2. 2.Division of Population Health Sciences and EducationSt. George’s, University of LondonLondonUK

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