Full Text:   <2629>

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CLC number: TP391

On-line Access: 2019-08-29

Received: 2017-06-19

Revision Accepted: 2018-03-09

Crosschecked: 2019-08-15

Cited: 0

Clicked: 5047

Citations:  Bibtex RefMan EndNote GB/T7714


Lan-yan Xue


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Frontiers of Information Technology & Electronic Engineering  2019 Vol.20 No.8 P.1075-1086


A saliency and Gaussian net model for retinal vessel segmentation

Author(s):  Lan-yan Xue, Jia-wen Lin, Xin-rong Cao, Shao-hua Zheng, Lun Yu

Affiliation(s):  College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China; more

Corresponding email(s):   xuelanyan@126.com

Key Words:  Retinal vessel segmentation, Saliency model, Gaussian net (GNET), Feature learning

Lan-yan Xue, Jia-wen Lin, Xin-rong Cao, Shao-hua Zheng, Lun Yu. A saliency and Gaussian net model for retinal vessel segmentation[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1075-1086.

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journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A saliency and Gaussian net model for retinal vessel segmentation
%A Lan-yan Xue
%A Jia-wen Lin
%A Xin-rong Cao
%A Shao-hua Zheng
%A Lun Yu
%J Frontiers of Information Technology & Electronic Engineering
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1700404

T1 - A saliency and Gaussian net model for retinal vessel segmentation
A1 - Lan-yan Xue
A1 - Jia-wen Lin
A1 - Xin-rong Cao
A1 - Shao-hua Zheng
A1 - Lun Yu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1700404

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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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