Small Target Recognition Using Dynamic Time Warping and Visual Attention

Abstract

Abstract Microaneurysm is a kind of small targets in color retinal image, and it is an essential work to recognize the small target for the early diagnosis of diabetic retinopathy. This paper proposes an efficient method to accurately recognize microaneurysm. A symmetric extended curvature Gabor wavelet is presented to generate candidate objects, where some novel features are extracted for classification. A kind of statistic features is generated to distinguish between microaneurysm and thin vessels, in terms of the shape similarity of cross-section profiles. Furthermore, the visual attention-based features are proposed to compute local contrast of small targets in complex background. Random undersampling with AdaBoost (RUSBoost) classifier is employed to discriminate true microaneurysm from an overwhelming amount of candidate objects. Experimental results demonstrate that the proposed method achieves significant sensitivity and accuracy on the public datasets, in comparison to the state-of-the-arts.