Author/Authors :
Zhou, Wei Northeastern University - Shenyang - Liaoning, China , Wu, Chengdong 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 :
Red lesions can be regarded as one of the earliest lesions in diabetic retinopathy (DR) and automatic detection of red lesions plays
a critical role in diabetic retinopathy diagnosis. In this paper, a novel superpixel Multichannel Multifeature (MCMF) classification
approach is proposed for red lesion detection. In this paper, firstly, a new candidate extraction method based on superpixel is
proposed. Then, these candidates are characterized by multichannel features, as well as the contextual feature. Next, FDA classifier
is introduced to classify the red lesions among the candidates. Finally, a postprocessing technique based on multiscale blood
vessels detection is modified for removing nonlesions appearing as red. Experiments on publicly available DiaretDB1 database
are conducted to verify the effectiveness of our proposed method.
