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Comput Med Imaging Graph. 2017 Jan;55:106-112. doi: 10.1016/j.compmedimag.2016.08.001. Epub 2016 Aug 4.

Automatic detection of microaneurysms in retinal fundus images.

Author information

1
School of Electronic and Information Engineering, Soochow University, Suzhou, China.
2
School of Electronic and Information Engineering, Soochow University, Suzhou, China. Electronic address: xjchen@suda.edu.cn.

Abstract

Diabetic retinopathy (DR) is one of the leading causes of new cases of blindness. Early and accurate detection of microaneurysms (MAs) is important for diagnosis and grading of diabetic retinopathy. In this paper, a new method for the automatic detection of MAs in eye fundus images is proposed. The proposed method consists of four main steps: preprocessing, candidate extraction, feature extraction and classification. A total of 27 characteristic features which contain local features and profile features are extracted for KNN classifier to distinguish true MAs from spurious candidates. The proposed method has been evaluated on two public database: ROC and e-optha. The experimental result demonstrates the efficiency and effectiveness of the proposed method, and it has the potential to be used to diagnose DR clinically.

KEYWORDS:

Classifier; Diabetic retinopathy (DR); Eye fundus images; Local features; Microaneurysms (MAs); Profile features

[Indexed for MEDLINE]

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