Automated microaneurysm detection in diabetic retinopathy using curvelet transform

• Published in: Journal of biomedical optics Vol. 21; no. 10; p. 101404
• Main Authors: Ali Shah, Syed Ayaz, Laude, Augustinus, Faye, Ibrahima, Tang, Tong Boon
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.10.2016
• Subjects: Aneurysm - diagnosis; Aneurysm - pathology; Diabetic Retinopathy - complications; Diabetic Retinopathy - pathology; Fundus Oculi; Humans; Image Processing, Computer-Assisted - methods; Signal Processing, Computer-Assisted; microaneurysms; diabetic retinopathy; fundus images; curvelet transform
• ISSN: 1083-3668; 1560-2281
• DOI: 10.1117/1.JBO.21.10.101404

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.