Journal
PATTERN RECOGNITION
Volume 46, Issue 8, Pages 2117-2133Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2012.12.014
Keywords
Vessel detection; Retinal images; Segmentation; Matched filter; Multiwavelet; Multiscale hierarchical decomposition
Funding
- China Scholarship Council (CSC)
- National High-Tech R&D Program (863 Program) [2012AA10A412]
- Leverhulme research fellowship [RF/9/REG/2009/0507]
- University of Science and Technology Beijing [06108038]
- Scottish Northern Research Partnership
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We propose a comprehensive method for segmenting the retinal vasculature in fundus camera images. Our method does not require preprocessing and training and can therefore be used directly on different images sets. We enhance the vessels using matched filtering with multiwavelet kernels (MFMK), separating vessels from clutter and bright, localized features. Noise removal and vessel localization are achieved by a multiscale hierarchical decomposition of the normalized enhanced image. We show a necessary condition to achieve the optimal decomposition and derive the associated value of the scale parameter controlling the amount of details captured. Finally, we obtain a binary map of the vasculature by locally adaptive thresholding, generating a threshold surface based on the vessel edge information extracted by the previous processes. We report experimental results on two public retinal data sets, DRIVE and STARE, demonstrating an excellent performance in comparison with retinal vessel segmentation methods reported recently. (C) 2013 Elsevier Ltd. All rights reserved.
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