Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12091
Title: Microaneurysm (MA) detection via sparse representation classifier with MA and non-MA dictionary learning
Authors: Zhang, B
Zhang, L 
You, J 
Karray, F
Keywords: Sparse Representation Classifier
Diabetic retinopathy
Microaneurysm
Issue Date: 2010
Publisher: IEEE
Source: 2010 20th International Conference on Pattern Recognition (ICPR), 23-26 August 2010, Istanbul, p. 277-280 How to cite?
Abstract: Diabetic retinopathy (DR) is a common complication of diabetes that damages the retina and leads to sight loss if treated late. In its earliest stage, DR can be diagnosed by micro aneurysm (MA). Although some algorithms have been developed, the accurate detection of MA in color retinal images is still a challenging problem. In this paper we propose a new method to detect MA based on Sparse Representation Classifier (SRC). We first roughly locate MA candidates by using multi-scale Gaussian correlation filtering, and then classify these candidates with SRC. Particularly, two dictionaries, one for MA and one for non-MA, are learned from example MA and non-MA structures, and are used in the SRC process. Experimental results on the ROC database show that the proposed method can well distinguish MA from non-MA objects.
URI: http://hdl.handle.net/10397/12091
ISBN: 978-1-4244-7542-1
ISSN: 1051-4651
DOI: 10.1109/ICPR.2010.77
Appears in Collections:Conference Paper

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