Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (8): 19-24.doi: 10.16180/j.cnki.issn1007-7820.2021.08.004

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Fundus Blood Vessel Image Segmentation Combining Resnet50 and U-Net

SI Mingming,CHEN Wei,HU Chunyan,YIN Zhong   

  1. School of Optical Electrical and Computer Engineering,University of Shanghai for Science and Technology, Shanghai 200093, China
  • Received:2020-03-23 Online:2021-08-15 Published:2021-08-17
  • Supported by:
    National Natural Science Foundation of China(61703277)


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