Comparative performance of Texton based vascular tree segmentation in retinal images

Zhang, Lei, Fisher, Mark and Wang, Wenjia (2015) Comparative performance of Texton based vascular tree segmentation in retinal images. In: 2014 IEEE International Conference on Image Processing (ICIP). The Institute of Electrical and Electronics Engineers (IEEE), FRA, pp. 952-956.

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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.

Item Type:	Book Section
Uncontrolled Keywords:	image segmentation,texton,filter bank,clustering
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Interactive Graphics and Audio Faculty of Science > Research Groups > Data Science and AI
Depositing User:	Pure Connector
Date Deposited:	07 Jan 2017 00:02
Last Modified:	24 Sep 2024 08:05
URI:	https://ueaeprints.uea.ac.uk/id/eprint/61969
DOI:	10.1109/ICIP.2014.7025191

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