Automatic detection of microaneurysms in retinal fundus images

- An automatic method for detection of microaneurysms in fundus images is proposed.
- A robust preprocessing method is applied.
- An effective candidate extraction method is used.
- A total of 27 features which contain not only profile features but also local features are used for classification.
- The KNN (k = 14) classifier is selected for classification.

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.