融合显著性模型和高斯网模型的视网膜血管分割方法

薛岚燕; 林嘉雯; 曹新容; 郑绍华; 余轮

doi:10.1631/FITEE.1700404

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融合显著性模型和高斯网模型的视网膜血管分割方法

常规文章 | Updated：2022-05-19

- 融合显著性模型和高斯网模型的视网膜血管分割方法
- A saliency and Gaussian net model for retinal vessel segmentation
- 信息与电子工程前沿（英文） 2019年20卷第8期页码：1075-1086
- Affiliations：
  
  College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
  Institute of Computer and Information, Fujian Agriculture and Forestry University, Fuzhou 350002, China
- Author bio：
  
  Lan-yan XUE, E-mail: xuelanyan@126.com
- Funds：
  
  Project supported by the Natural Science Foundation of Fujian Province, China (No. 2016J0129) and the Educational Commission of Fujian Province of China (No. JAT170180)
- DOI：10.1631/FITEE.1700404
  中图分类号： TP391
- 收稿：2017-06-19，
  
  修回：2019-;8-15，
  
  纸质出版：2019-08
- Accepted：
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薛岚燕, 林嘉雯, 曹新容, 等. 融合显著性模型和高斯网模型的视网膜血管分割方法[J]. 信息与电子工程前沿（英文）, 2019,20(8):1075-1086.

Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, et al. A saliency and Gaussian net model for retinal vessel segmentation[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1075-1086.
薛岚燕, 林嘉雯, 曹新容, 等. 融合显著性模型和高斯网模型的视网膜血管分割方法[J]. 信息与电子工程前沿（英文）, 2019,20(8):1075-1086. DOI： 10.1631/FITEE.1700404.

Lan-yan XUE, Jia-wen LIN, Xin-rong CAO, et al. A saliency and Gaussian net model for retinal vessel segmentation[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(8): 1075-1086. DOI： 10.1631/FITEE.1700404.

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。

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.

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references

R Achanta , , , S Hemami , , , F Estrada , , , 等 . . Frequency- tuned salient region detection . . Proc IEEE Conf on Computer Vision and Pattern Recognition , , 2009 . . p.1597 - - 1604 . . DOI: 10.1109/CVPR.2009.5206596 http://doi.org/10.1109/CVPR.2009.5206596 . .

G Ayala , , , T Leon , , , V Zapater . . Different averages of a fuzzy set with an application to vessel segmentation . . IEEE Trans Fuzzy Syst , , 2005 . . 13 ( ( 3 ): ): 384 - - 393 . . DOI: 10.1109/TFUZZ.2004.839667 http://doi.org/10.1109/TFUZZ.2004.839667 . .

S Chaudhuri , , , S Chatterjee , , , N Katz , , , 等 . . Detection of blood vessels in retinal images using two-dimensional matched filters . . IEEE Trans Med Imag , , 1989 . . 8 ( ( 3 ): ): 263 - - 269 . . DOI: 10.1109/42.34715 http://doi.org/10.1109/42.34715 . .

SW Franklin , , , SE Rajan . . Retinal vessel segmentation employing ANN technique by Gabor and moment invariants-based features . . Appl Soft Comput , , 2014 . . 22 94 - - 100 . . DOI: 10.1016/j.asoc.2014.04.024 http://doi.org/10.1016/j.asoc.2014.04.024 . .

HZ Fu , , , XC Cao , , , ZW Tu . . Cluster-based co-saliency detection . . IEEE Trans Imag Process , , 2013 . . 22 ( ( 10 ): ): 3766 - - 3778 . . DOI: 10.1109/TIP.2013.2260166 http://doi.org/10.1109/TIP.2013.2260166 . .

HZ Fu , , , YW Xu , , , DW Kee , , , 等 . . Retinal vessel segmentation via deep learning network and fully-connected conditional random fields . . Proc IEEE 13 th Int Symp on Biomedical Imaging , , 2016 . . p.698 - - 701 . . DOI: 10.1109/ISBI.2016.7493362 http://doi.org/10.1109/ISBI.2016.7493362 . .

XT He , , , YX Peng , , , JJ Zhao . . Fine-grained discriminative localization via saliency-guided faster R-CNN . . Proc 25 th ACM Int Conf on Multimedia , , 2017 . . p.627 - - 635 . . DOI: 10.1145/3123266.3123319 http://doi.org/10.1145/3123266.3123319 . .

YT Hu , , , NN Wang , , , DC Tao , , , 等 . . SERF: a simple, effective, robust, and fast image super-resolver from cascaded linear regression . . IEEE Trans Imag Process , , 2016 . . 25 ( ( 9 ): ): 4091 - - 4102 . . DOI: 10.1109/TIP.2016.2580942 http://doi.org/10.1109/TIP.2016.2580942 . .

MK Ikram , , , FJ de Jong , , , MJ Bos , , , 等 . . Retinal vessel diameters and risk of stroke: the Rotterdam study . . Neurology , , 2006 . . 66 ( ( 9 ): ): 1339 - - 1343 . . DOI: 10.1212/01.wnl.0000210533.24338.ea http://doi.org/10.1212/01.wnl.0000210533.24338.ea . .

E Imani , , , HR Pourreza . . A novel method for retinal exudate segmentation using signal separation algorithm . . Comput Method Program Biomed , , 2016 . . 133 195 - - 205 . . DOI: 10.1016/j.cmpb.2016.05.016 http://doi.org/10.1016/j.cmpb.2016.05.016 . .

RP Kumar , , , F Albregtsen , , , M Reimers , , , 等 . . Blood vessel segmentation and centerline tracking using local structure analysis . . Proc 6 th European Conf of the Int Federation for Medical and Biological Engineering , , 2015 . . p.122 - - 125 . . DOI: 10.1007/978-3-319-11128-5_31 http://doi.org/10.1007/978-3-319-11128-5_31 . .

I Liu , , , Y Sun . . Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme . . IEEE Trans Med Imag , , 1993 . . 12 ( ( 2 ): ): 334 - - 341 . . DOI: 10.1109/42.232264 http://doi.org/10.1109/42.232264 . .

Z Liu , , , WB Zou , , , LN Li , , , 等 . . Co-saliency detection based on hierarchical segmentation . . IEEE Signal Process Lett , , 2014 . . 21 ( ( 1 ): ): 88 - - 92 . . DOI: 10.1109/LSP.2013.2292873 http://doi.org/10.1109/LSP.2013.2292873 . .

D Maji , , , A Santara , , , S Ghosh , , , 等 . . Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images . . Proc 37 th Annual Int Conf of the IEEE Engineering in Medicine and Biology Society , , 2015 . . p.3029 - - 3032 . . DOI: 10.1109/EMBC.2015.7319030 http://doi.org/10.1109/EMBC.2015.7319030 . .

J Odstrcilik , , , K Radim , , , B Attila , , , 等 . . Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database . . IET Image Process , , 2013 . . 7 ( ( 4 ): ): 373 - - 383 . . DOI: 10.1049/iet-ipr.2012.0455 http://doi.org/10.1049/iet-ipr.2012.0455 . .

YX Peng , , , XT He , , , JJ Zhao . . Object-part attention model for fine-grained image classification . . IEEE Trans Imag Process , , 2018 . . 27 ( ( 3 ): ): 1487 - - 1500 . . DOI: 10.1109/TIP.2017.2774041 http://doi.org/10.1109/TIP.2017.2774041 . .

O Ronneberger , , , P Fischer , , , T Brox . . U-Net: convolutional networks for biomedical image segmentation . . Proc 18 th Int Conf on Medical Image Computing and Computer- Assisted Intervention , , 2015 . . p.234 - - 241 . . DOI: 10.1007/978-3-319-24574-4_28 http://doi.org/10.1007/978-3-319-24574-4_28 . .

J Schmidhuber . . Deep learning in neural networks: an overview . . Neur Netw , , 2015 . . 61 85 - - 117 . . DOI: 10.1016/j.neunet.2014.09.003 http://doi.org/10.1016/j.neunet.2014.09.003 . .

E Shelhamer , , , J Long , , , T Darrell . . Fully convolutional networks for semantic segmentation . . IEEE Trans Patt Anal Mach Intell , , 2017 . . 39 ( ( 4 ): ): 640 - - 651 . . DOI: 10.1109/TPAMI.2016.2572683 http://doi.org/10.1109/TPAMI.2016.2572683 . .

NH Solouma , , , ABM Youssef , , , YA Badr , , , 等 . . A new real-time retinal tracking system for image-guided laser treatment . . IEEE Trans Biomed Eng , , 2002 . . 49 ( ( 9 ): ): 1059 - - 1067 . . DOI: 10.1109/TBME.2002.802059 http://doi.org/10.1109/TBME.2002.802059 . .

LA Vese , , , TF Chan . . A multiphase level set framework for image segmentation using the Mumford and Shah model . . Int J Comput Vis , , 2002 . . 50 ( ( 3 ): ): 271 - - 293 . . DOI: 10.1023/A:1020874308076 http://doi.org/10.1023/A:1020874308076 . .

NN Wang , , , XB Gao , , , LY Sun , , , 等 . . Bayesian face sketch synthesis . . IEEE Trans Imag Process , , 2017 . . 26 ( ( 3 ): ): 1264 - - 1274 . . DOI: 10.1109/TIP.2017.2651375 http://doi.org/10.1109/TIP.2017.2651375 . .

NN Wang , , , XB Gao , , , LY Sun , , , 等 . . Anchored neighborhood index for face sketch synthesis . . IEEE Trans Circ Syst Video Technol , , 2018 . . 28 ( ( 9 ): ): 2154 - - 2163 . . DOI: 10.1109/TCSVT.2017.2709465 http://doi.org/10.1109/TCSVT.2017.2709465 . .

XH Wang , , , YQ Zhao , , , M Liao , , , 等 . . Automatic segmentation for retinal vessel based on multi-scale 2D Gabor wavelet . . Acta Automat Sin , , 2015 . . 41 ( ( 5 ): ): 970 - - 980 . . DOI: 10.16383/j.aas.2015.c140185 http://doi.org/10.16383/j.aas.2015.c140185 . .

TJ Xiao , , , YC Xu , , , KY Yang , , , 等 . . The application of two-level attention models in deep convolutional neural network for fine-grained image classification . . Proc IEEE Conf on Computer Vision and Pattern Recognition , , 2015 . . p.842 - - 850 . . DOI: 10.1109/CVPR.2015.7298685 http://doi.org/10.1109/CVPR.2015.7298685 . .

F Zana , , , JC Klein . . Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation . . IEEE Trans Imag Process , , 2001 . . 10 ( ( 7 ): ): 1010 - - 1019 . . DOI: 10.1109/83.931095 http://doi.org/10.1109/83.931095 . .

YQ Zhao , , , XH Wang , , , XF Wang , , , 等 . . Retinal vessels segmentation based on level set and region growing . . Patt Recog , , 2014 . . 47 ( ( 7 ): ): 2437 - - 2446 . . DOI: 10.1016/j.patcog.2014.01.006 http://doi.org/10.1016/j.patcog.2014.01.006 . .

CZ Zhu , , , BJ Zou , , , Y Xiang , , , 等 . . A survey of retinal vessel segmentation in fundus images . . J Comput Aided Des Comput Graph , , 2015 . . 27 ( ( 11 ): ): 2046 - - 2057 . . . .

CZ Zhu , , , BJ Zou , , , Y Xiang , , , 等 . . An ensemble retinal vessel segmentation based on supervised learning in fundus images . . Chin J Electron , , 2016 . . 25 ( ( 3 ): ): 503 - - 511 . . DOI: 10.1049/cje.2016.05.016 http://doi.org/10.1049/cje.2016.05.016 . .

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