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Journal of Biomedical Optics • Open Access

Automated microaneurysm detection in diabetic retinopathy using curvelet transform
Author(s): Syed Ayaz Ali Shah; Augustinus Laude; Ibrahima Faye; Tong Boon Tang

Paper Abstract

Microaneurysms (MAs) are known to be the early signs of diabetic retinopathy (DR). An automated MA detection system based on curvelet transform is proposed for color fundus image analysis. Candidates of MA were extracted in two parallel steps. In step one, blood vessels were removed from preprocessed green band image and preliminary MA candidates were selected by local thresholding technique. In step two, based on statistical features, the image background was estimated. The results from the two steps allowed us to identify preliminary MA candidates which were also present in the image foreground. A collection set of features was fed to a rule-based classifier to divide the candidates into MAs and non-MAs. The proposed system was tested with Retinopathy Online Challenge database. The automated system detected 162 MAs out of 336, thus achieved a sensitivity of 48.21% with 65 false positives per image. Counting MA is a means to measure the progression of DR. Hence, the proposed system may be deployed to monitor the progression of DR at early stage in population studies.

Paper Details

Date Published: 11 February 2016
PDF: 8 pages
J. Biomed. Opt. 21(10) 101404 doi: 10.1117/1.JBO.21.10.101404
Published in: Journal of Biomedical Optics Volume 21, Issue 10
Show Author Affiliations
Syed Ayaz Ali Shah, Univ. Teknologi Petronas (Malaysia)
Augustinus Laude, National Healthcare Group Eye Institute (Singapore)
Ibrahima Faye, Univ. Teknologi Petronas (Malaysia)
Tong Boon Tang, Univ. Teknologi Petronas (Malaysia)

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