Journal
IEEE ACCESS
Volume 5, Issue -, Pages 2563-2572Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2017.2671918
Keywords
Diabetic retinopathy; microaneurysm detection; sparse PCA; unsupervised classification
Categories
Funding
- National Natural Science Foundation of China [61273078, 61471110, 61602221]
- Foundation of Liaoning Educational Department [L2014090]
- Fundamental Research Funds for the Central Universities [N140403005]
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Since microaneurysms (MAs) can be seen as the earliest lesions in diabetic retinopathy, its detection plays a critical role in the diabetic retinopathy diagnosis. In recent years, many machine-learning methods have been developed for MA detection. Generally, MA candidates are first identified and then a set of features for these candidates are extracted. Finally, machine-learning methods are applied for candidate classification. In this paper, we present a novel unsupervised classification method based on sparse posterior cerebral artery (PCA) for MA detection. Since it does not have to consider a non-MA training set, the class imbalance problem can be avoided. Furthermore, effective features can be selected due to the characteristic of sparse PCA, which combines the elastic net penalty with the PCA. Meanwhile, a single T-2 statistic is introduced, and the control limit can be determined for distinguishing true MAs from spurious candidates automatically. Experiment results on the retinopathy online challenge competition database show the effectiveness of our proposed method.
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