Automated microaneurysm detection in diabetic retinopathy using curvelet transform

Ali Shah, S.A. and Laude, A. and Faye, I. and Tang, T.B. (2016) Automated microaneurysm detection in diabetic retinopathy using curvelet transform. Journal of Biomedical Optics, 21 (10).

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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. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Item Type:Article
Impact Factor:cited By 22
Uncontrolled Keywords:Automation; Blood vessels; Image segmentation, Curvelet transforms; Diabetic retinopathy; Fundus image; Local thresholding; Microaneurysms; Population studies; Rule-based classifier; Statistical features, Eye protection, aneurysm; complication; diabetic retinopathy; eye fundus; human; image processing; pathology; procedures; signal processing, Aneurysm; Diabetic Retinopathy; Fundus Oculi; Humans; Image Processing, Computer-Assisted; Signal Processing, Computer-Assisted
ID Code:25689
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:27 Aug 2021 09:40
Last Modified:27 Aug 2021 09:40

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