NDLI: Comparative performance of texton based vascular tree segmentation in retinal images

Content Provider	IEEE Xplore Digital Library
Author	Zhang, Lei Fisher, Mark Wang, Wenjia
Copyright Year	2014
Description	Author affiliation: University of East Anglia, Norwich Research Park, Norwich, NR4 7TJ UK (Zhang, Lei; Fisher, Mark; Wang, Wenjia)
Abstract	This paper considers the problem of vessel segmentation in optical fundus images of the retina. We adopt an approach that uses a machine learning paradigm to identify texture features called textons and present a new filter bank (MR11) that includes bar detectors for vascular feature extraction and other kernels to detect edges and photometric variations in the image. Textons are generated by k-means clustering and texton maps representing vessels are derived by back-projecting pixel clusters onto hand labelled ground truth. A further step is implemented to ensure that the best combinations of textons are represented in the map and subsequently used to identify vessels in the test set. The experimental results on two benchmark datasets show that our proposed method performs well compared to other published work and the results of human experts. A further test of our system on an independent set of optical fundus images verified its consistent performance.
Starting Page	952
Ending Page	956
File Size	402471
Page Count	5
File Format	PDF
ISBN	9781479957514
DOI	10.1109/ICIP.2014.7025191
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-10-27
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filter banks Image segmentation Retina Optical filters Accuracy Matched filters Biomedical imaging clustering image segmentation texton filter bank
Content Type	Text
Resource Type	Article

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