期刊
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
卷 20, 期 8, 页码 1075-1086出版社
ZHEJIANG UNIV PRESS
DOI: 10.1631/FITEE.1700404
关键词
Retinal vessel segmentation; Saliency model; Gaussian net (GNET); Feature learning; TP391
类别
资金
- Natural Science Foundation of Fujian Province, China [2016J0129]
- Educational Commission of Fujian Province of China [JAT170180]
Retinal vessel segmentation is a significant problem in the analysis of fundus images. A novel deep learning structure called the Gaussian net (GNET) model combined with a saliency model is proposed for retinal vessel segmentation. A saliency image is used as the input of the GNET model replacing the original image. The GNET model adopts a bilaterally symmetrical structure. In the left structure, the first layer is upsampling and the other layers are max-pooling. In the right structure, the final layer is max-pooling and the other layers are upsampling. The proposed approach is evaluated using the DRIVE database. Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models. The proposed algorithm performs well in extracting vessel networks, and is more accurate than other deep learning methods. Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.
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