Author/Authors :
Zhou, Wei Faculty of Robot Science and Engineering - Northeastern University - Shenyang - Liaoning, China , Wu, Chengdong Faculty of Robot Science and Engineering - Northeastern University - Shenyang - Liaoning, China , Chen, Dali Northeastern University - Shenyang - Liaoning, China , Wang, Zhenzhu Northeastern University - Shenyang - Liaoning, China , Yi, Yugen School of Software - Jiangxi Normal University - Nanchang - Jiangxi, China , Du, Wenyou Northeastern University - Shenyang - Liaoning, China
Abstract :
Recently, microaneurysm (MA) detection has attracted a lot of attention in the medical image processing community. Since MAs
can be seen as the earliest lesions in diabetic retinopathy, their detection plays a critical role in diabetic retinopathy diagnosis. In
this paper, we propose a novel MA detection approach named multifeature fusion dictionary learning (MFFDL). The proposed
method consists of four steps: preprocessing, candidate extraction, multifeature dictionary learning, and classification. The novelty
of our proposed approach lies in incorporating the semantic relationships among multifeatures and dictionary learning into a
unified framework for automatic detection of MAs. We evaluate the proposed algorithm by comparing it with the state-of-the-art
approaches and the experimental results validate the effectiveness of our algorithm.
