融合Resnet50和U-Net的眼底彩色血管图像分割

doi:10.16180/j.cnki.issn1007-7820.2021.08.004

Abstract

Abstract:

The characteristic information of the fundus blood vessel image has high complexity, and the existing algorithms have the problems of insufficient retinal microvascular segmentation and wrong segmentation of the focus area. In view of these problems,a fundus color blood vessel image segmentation model combined RESNET 50 and U-Net is proposed. Through data enhancement, the number of datasets is increased, so as to improve the robustness of the algorithm. The adaptive Gamma correction is used to improve the brightness information and reduce the interference of the image artifacts. In the coding part, the residual network is used to extract the deep-seated features of the color image. After four times up-sampling and skip-connection of the feature map, pixel level segmentation is achieved. In each convolution layer, the ReLU activation function and batch normalization are used to improve the performance of the model. A large number of experiments in the open dataset DRIVE show that the specificity of the proposed algorithm is 0.989 2, the accuracy is 0.967 6, and the sensitivity is 0.812 0.

Key words: retinal blood vessel, neural network, data enhancement, image preprocessing, bilateral filtering, feature extraction, image segmentation, the residual network, U-net

CLC Number:

TP391.4

SI Mingming,CHEN Wei,HU Chunyan,YIN Zhong. Fundus Blood Vessel Image Segmentation Combining Resnet50 and U-Net[J].Electronic Science and Technology, 2021, 34(8): 19-24.

Figures/Tables 7

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References 19

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算法来源	年份	灵敏度	特异性	准确率
文献[14]	2014	0.725 2	0.979 8	0.947 4
文献[15]	2016	0.756 9	0.981 6	0.952 7
文献[16]	2016	0.773 1	0.972 4	0.946 7
文献[17]	2016	0.766 3	0.976 8	0.949 5
文献[6]	2017	0.789 7	0.968 4	-
文献[18]	2018	0.806 3	0.977 8	0.955 6
文献[19]	2018	0.780 2	0.987 6	0.963 6
本文算法	2020	0.812 0	0.989 2	0.967 6

Fundus Blood Vessel Image Segmentation Combining Resnet50 and U-Net

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