Retinal vessel segmentation using Gabor Filter and Textons

Zhang, Lei, Fisher, Mark and Wang, Wenjia (2014) Retinal vessel segmentation using Gabor Filter and Textons. In: Medical Image Understanding and Analysis (MIUA 2014), 2014-07-09 - 2014-07-11, Royal Holloway College.

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Abstract

This paper presents a retinal vessel segmentation method that is inspired by the human visual system and uses a Gabor filter bank. Machine learning is used to optimize the filter parameters for retinal vessel extraction. The filter responses are represented as textons and this allows the corresponding membership functions to be used as the framework for learning vessel and non-vessel classes. Then, vessel texton memberships are used to generate segmentation results. We evaluate our method using the publicly available DRIVE database. It achieves competitive performance (sensitivity=0.7673, specificity=0.9602, accuracy=0.9430) compared to other recently published work. These figures are particularly interesting as our filter bank is quite generic and only includes Gabor responses. Our experimental results also show that the performance, in terms of sensitivity, is superior to other methods.

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences

University of East Anglia > Faculty of Science > Research Groups > Computational Biology (subgroups are shown below) > Machine learning in computational biology
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Depositing User: Pure Connector
Date Deposited: 25 Feb 2015 06:21
Last Modified: 22 Apr 2020 09:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/52318
DOI:

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