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Artery/vein classification using reflection features in retina fundus images.
- Source :
-
Machine Vision & Applications . Jan2018, Vol. 29 Issue 1, p23-34. 12p. - Publication Year :
- 2018
-
Abstract
- Automatic artery/vein (A/V) classification is one of the important topics in retinal image analysis. It allows the researchers to investigate the association between biomarkers and disease progression on a huge amount of data for arteries and veins separately. Recent proposed methods, which employ contextual information of vessels to achieve better A/V classification accuracy, still rely on the performance of pixel-wise classification, which has received limited attention in recent years. In this paper, we show that these classification methods can be markedly improved. We propose a new normalization technique for extracting four new features which are associated with the lightness reflection of vessels. The accuracy of a linear discriminate analysis classifier is used to validate these features. Accuracy rates of 85.1, 86.9 and 90.6% were obtained on three datasets using only local information. Based on the introduced features, the advanced graph-based methods will achieve a better performance on A/V classification. [ABSTRACT FROM AUTHOR]
- Subjects :
- *RETINAL imaging
*DISEASE progression
*BLOOD vessels
*PIXELS
*MACULA lutea
Subjects
Details
- Language :
- English
- ISSN :
- 09328092
- Volume :
- 29
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Machine Vision & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 127147462
- Full Text :
- https://doi.org/10.1007/s00138-017-0867-x