A Retinal Vessel Image Segmentation Method Incorporating Dual Attention Mechanism and U-net Model
Retinal vascular segmentation has important clinical value for the diagnosis and treatment of retinal related diseases,but retinal im-ages are complex and diverse,making its processing challenging.Therefore,to address the issue of insufficient accuracy in retinal vessel seg-mentation,a retinal vessel segmentation method is proposed that integrates coordinate attention mechanism CA and a novel parameterless at-tention mechanism SimAM with U-net model based on encoding decoding structure.This method improves the contrast between blood vessels and surrounding tissues through preprocessing operations such as image grayscale,contrast limitation,histogram equalization,standardiza-tion,and gamma correction;Using overlapping tile strategy to segment the image into sub blocks to increase the amount of data;In the downs-ampling process of the U-net model,the coordinate attention mechanism CA is introduced,and in the upsampling process,a new parameter-less attention mechanism SimAM is introduced.By training and constructing a network model to segment the test samples,the segmentation re-sults of retinal vessel images are obtained.The average accuracy of the model on the DRIVE dataset is 97.68%,the average sensitivity is 80.62%,the average specificity is 98.17%,and the AUC coefficient is 0.982 4;Meanwhile,compared with the control method for retinal vas-cular image segmentation,it shows better segmentation performance.The fusion of dual attention mechanism and U-net model segmentation method can effectively improve the performance of retinal vessel segmentation.
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