Tram-Line filtering for retinal vessel segmentation

Hunter, Andrew and Lowell, James and Ryder, Robert and Basu, Ansu and Steel, David (2005) Tram-Line filtering for retinal vessel segmentation. In: 3rd European Medical and Biological Engineering Conference, 20-24 November 2005, Prague, Czech Republic.

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Tram-line filtering for retinal vessel segmentation
Tram-line filtering for retinal vessel segmentation
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Abstract

The segmentation of the vascular network from retinal fundal images is a fundamental step in the analysis of the retina, and may be used for a number of purposes, including diagnosis of diabetic retinopathy. However, due to the variability of retinal images segmentation is difficult, particularly with images of diseased retina which include significant distractors.
This paper introduces a non-linear filter for vascular segmentation, which is particularly robust against such distractors. We demonstrate results on the publicly-available STARE dataset, superior to Stare’s performance, with 57.2% of the vascular network (by length) successfully located, with 97.2% positive predictive value measured by vessel length, compared with 57% and 92.2% for Stare. The filter is also simple and computationally efficient.

Item Type:Conference or Workshop Item (Paper)
Additional Information:The segmentation of the vascular network from retinal fundal images is a fundamental step in the analysis of the retina, and may be used for a number of purposes, including diagnosis of diabetic retinopathy. However, due to the variability of retinal images segmentation is difficult, particularly with images of diseased retina which include significant distractors. This paper introduces a non-linear filter for vascular segmentation, which is particularly robust against such distractors. We demonstrate results on the publicly-available STARE dataset, superior to Stare’s performance, with 57.2% of the vascular network (by length) successfully located, with 97.2% positive predictive value measured by vessel length, compared with 57% and 92.2% for Stare. The filter is also simple and computationally efficient.
Keywords:Retinal, Algorithm, retinal vessel segmentation, tram-line filtering, filtering for retinal
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:1908
Deposited By: Tammie Farley
Deposited On:26 Sep 2010 16:59
Last Modified:13 Mar 2013 08:32

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