Retinal blood vessel segmentation algorithm based on improved U-Net
The existing algorithms have the problem of low segmentation accuracy when facing small vessels in retinal images.In this paper,an improved U-Net segmentation algorithm is proposed by introducing residual module and detail enhancement attention mechanism module into U-Net network.In the coding and decoding stages,the residual module is used to replace the traditional convolutional module,which solves the problem of network degradation with increasing depth.Meanwhile,a detail enhancement attention mechanism is added between the encoder and the decoder to reduce the useless information in the output of the encoder,so that the sensitivity of the network to capture valid feature information is improved.In addition,the experimental results based on the standard image set DRIVE reveal that the segmentation accuracy,sensitivity and F1 score of the proposed algorithm are improved by 0.46%,2.14%and 1.56%,respectively compared to the U-Net,which is superior to the traditional segmentation algorithms.
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