基于密集循环网络的视网膜图像分割方法

杨云; 周舒婕; 李程辉; 张娟娟

doi:10.37188/CJLCD.2021-0142

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基于密集循环网络的视网膜图像分割方法

图像处理 | 更新时间：2021-12-10

- 基于密集循环网络的视网膜图像分割方法
- Retinal image segmentation method based on dense cycle networks
- 液晶与显示 2021年36卷第12期页码：1702-1711
- 作者机构：
  
  1.陕西科技大学人工智能研究所, 陕西西安 710021
  2.陕西科技大学电子信息与人工智能学院, 陕西西安 710021
- 作者简介：
  
  [ "杨云(1965-), 女, 陕西西安人, 博士, 教授, 2009年于陕西科技大学获得博士学位, 主要从事图像处理及智能信息处理方面的研究。E-mail: yangyun3190@163.com" ]
- 基金信息：
  
  国家自然科学基金(61971272);国家重点研发计划重点专项(2019YFC1520204);国家自然科学基金青年科学基金(61601271);陕西省教育厅专项科研计划(15JK1086)
- DOI：10.37188/CJLCD.2021-0142
  中图分类号： TP391.4;TP183
- 收稿日期：2021-05-22，
  
  修回日期：2021-07-01，
  
  纸质出版日期：2021-12
- 稿件说明：
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杨云, 周舒婕, 李程辉, 等. 基于密集循环网络的视网膜图像分割方法[J]. 液晶与显示, 2021,36(12):1702-1711.

Yun YANG, Shu-jie ZHOU, Cheng-hui LI, et al. Retinal image segmentation method based on dense cycle networks[J]. Chinese journal of liquid crystals and displays, 2021, 36(12): 1702-1711.
杨云, 周舒婕, 李程辉, 等. 基于密集循环网络的视网膜图像分割方法[J]. 液晶与显示, 2021,36(12):1702-1711. DOI： 10.37188/CJLCD.2021-0142.

Yun YANG, Shu-jie ZHOU, Cheng-hui LI, et al. Retinal image segmentation method based on dense cycle networks[J]. Chinese journal of liquid crystals and displays, 2021, 36(12): 1702-1711. DOI： 10.37188/CJLCD.2021-0142.

摘要

针对视网膜血管在分割过程容易出现细节特征信息丢失、血管轮廓模糊等问题，提出一种改进的循环分割对抗网络算法。该算法改进了分割器的网络模型，在U型网络上、下采样过程中添加了密集连接结构，充分保留了图像的特征信息，提升了模型的泛化能力以及鲁棒性，缓解了过度分割现象。为防止网络退化，将损失函数替换为最小二乘函数，提高了图像的分割质量，提升了网络模型训练的稳定性。实验结果表明，本文的网络模型在DRIVE以及CHASE数据集中，两者分割的准确性、敏感性分别达到了96.93%、84.30%以及96.94%、79.92%。该算法具有较好的网络泛化能力以及分割准确率，可以为疾病诊断提供重要的依据。

Abstract

Aiming at the problems of loss of detailed feature information and blurred contours of blood vessels in the segmentation process of retinal vessels

an improved cycle segmentation confrontation network algorithm is proposed. The algorithm improves the network model of the segmenter

adds dense connection structure in the up and down sampling processes of the U-Net network

fully retains the image feature information

which improves the generalization ability and robustness of the model

and alleviates the over segmentation phenomenon. In order to prevent network degradation

the loss function is replaced by the least square function

which improves the quality of image segmentation and the stability of network model training. The experimental results show that the segmentation accuracy and sensitivity of the network are 96.93%

84.30% and 96.94%

79.92% in the DRIVE and CHASE datasets respectively. The algorithm has good network generalization ability and segmentation accuracy

which can provide an important basis for disease diagnosis.

关键词

Keywords

references

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