Automatic detection of microaneurysms in retinal fundus images.
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.
School of Electronic and Information Engineering, Soochow University, Suzhou, China.
Bo
B
Wu
School of Electronic and Information Engineering, Soochow University, Suzhou, China.
Weifang
W
Zhu
School of Electronic and Information Engineering, Soochow University, Suzhou, China.
Fei
F
Shi
School of Electronic and Information Engineering, Soochow University, Suzhou, China.
Shuxia
S
Zhu
School of Electronic and Information Engineering, Soochow University, Suzhou, China. Electronic address: xjchen@suda.edu.cn.
Xinjian
X
Chen
D000465:Algorithms; D003930:Diabetic Retinopathy / Q000000981:diagnostic imaging*; D042241:Early Diagnosis; D005654:Fundus Oculi; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000071071:Microaneurysm / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*
Classifier; Diabetic retinopathy (DR); Eye fundus images; Local features; Microaneurysms (MAs); Profile features