Full Text:   <1819>

Summary:  <1202>

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: 3468

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Lan-yan Xue

http://orcid.org/0000-0003-2886-2983

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

http://doi.org/10.1631/FITEE.1700404


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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author="Lan-yan Xue, Jia-wen Lin, Xin-rong Cao, Shao-hua Zheng, Lun Yu",
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year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700404"
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%A Xin-rong Cao
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T1 - A saliency and Gaussian net model for retinal vessel segmentation
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Abstract: 
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.

融合显著性模型和高斯网模型的视网膜血管分割方法

摘要:视网膜血管分割是眼底图像分析的一个重要问题。本文提出一种融合显著性模型和高斯网(GNET)模型的新型深度学习结构分割视网膜血管。显著性图像替代原始图像作为GNET模型的输入。GNET模型具有双边对称结构。左边结构中,在第一层进行上采样操作,在其他层进行最大池化操作;右边结构中,在第一层进行最大池化操作,在其他层进行上采样操作。利用DRIVE数据库对所提方法进行评估。实验结果表明,与UNET模型相比,GNET模型能获得更精确的特征和更精细的细节。本文所提算法能提取准确的血管网络,与其他深度学习方法相比具有更高精确度。视网膜血管分割有助于提取血管变化特征,为脑血管疾病筛查提供依据。

关键词:视网膜血管分割;显著性模型;高斯网模型(GNET);特征学习

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

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