Technology of Graphic & Image
|
1247-1251

Improved U-Net fundus retinal vessels segmentation

Liang Liming
Sheng Xiaoqi
Guo Kai
Deng Guanghong
School of Electrical Engineering & Automation, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China

Abstract

In view of complexing feature information in retinal vessels, and the existing algorithms have low microvascular segmentation and pathological information mis-segmentation. Thus, this paper proposed a vessels segmentation model based on DenseNet and U-Net networks. First, it performed image enhancement by restricting contrast histogram equalization and filter filtering. Secondly, it used local adaptive gamma to improve retinal image brightness information and reduce artifact interference. Then, multi-scale morphological filtering locally enhanced microvascular feature information. Finally, it segmented the optimized vessel image using a U-shaped dense connection module. The algorithm has an average accuracy, sensitivity and specificity of 96.74%, 81.50% and 98.20% by experimenting on the DRIVE dataset.

Foundation Support

国家自然科学基金资助项目(51365017,61463018)
江西省自然科学基金面上项目(20192BAB205084)
江西省教育厅科学技术研究重点项目(GJJ170491)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.09.0775
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 4
Section: Technology of Graphic & Image
Pages: 1247-1251
Serial Number: 1001-3695(2020)04-062-1247-05

Publish History

[2020-04-05] Printed Article

Cite This Article

梁礼明, 盛校棋, 郭凯, 等. 基于改进的U-Net眼底视网膜血管分割 [J]. 计算机应用研究, 2020, 37 (4): 1247-1251. (Liang Liming, Sheng Xiaoqi, Guo Kai, et al. Improved U-Net fundus retinal vessels segmentation [J]. Application Research of Computers, 2020, 37 (4): 1247-1251. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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