Title
Unsupervised retinal vessel segmentation using combined filters
Author
Faculty/Department
Faculty of Sciences. Physics
Publication type
article
Publication
Subject
Engineering sciences. Technology
Source (journal)
PLoS ONE
Volume/pages
11(2016) :2 , 21 p.
ISSN
1932-6203
1932-6203
Article Reference
e0149943
Carrier
E-only publicatie
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Image segmentation of retinal blood vessels is a process that can help to predict and diagnose cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels' appearance. This work proposes an unsupervised method for the segmentation of retinal vessels images using a combined matched filter, Frangi's filter and Gabor Wavelet filter to enhance the images. The combination of these three filters in order to improve the segmentation is the main motivation of this work. We investigate two approaches to perform the filter combination: weighted mean and median ranking. Segmentation methods are tested after the vessel enhancement. Enhanced images with median ranking are segmented using a simple threshold criterion. Two segmentation procedures are applied when considering enhanced retinal images using the weighted mean approach. The first method is based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The experimental results demonstrate that the proposed methods perform well for vessel segmentation in comparison with state-of-the-art methods.
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Full text (open access)
https://repository.uantwerpen.be/docman/irua/4def94/132353.pdf
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