Fundus Vessel Segmentation Algorithm Based on Multi-feature Fusion Neural Network

Fundus Vessel Segmentation Algorithm Based on Multi-feature Fusion Neural Network

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In the 355 maybelline fit me early screening of ophthalmic diseases, diabetes, heart disease and other diseases, automatic mon-itoring of fundus capillaries occupies an important position.To solve the deficiency of blood vessel segmentation caused by the imprecise expression of capillary features, this paper proposes a multi-block residual neural network model (MbResU-Net) which uses the encoding-decoding network structure.In order to reduce the information loss caused by the semantic gap between network encoder and decoder, the model uses a nonlinear network structure instead of shortcut connections to be embedded in the network.In order to obtain the detailed features of fundus blood vessels, MbResU-Net connects three U-shaped networks in a residual block, and then extracts as osborne hog feeders for sale much as possible low-level features of the fundus tube on the premise of avoiding information loss.To ensure the seg-mentation quality, with the proper pre-processing images to the network, it designs a cross-entropy loss function incorporating the cost matrix to train the network parameters.

This paper compares MbResU-Net and the existing fundus blood vessel segmentation algorithms on the DRIVE and CHASE DB1 color fundus image datasets.Exper-iments show that MbResU-Net is superior to existing methods in Sen, ACC and AUC.The Sen is 0.7987 and 0.7972, ACC is 0.

9648 and 0.9726 and AUC is 0.9791 and 0.9824.Experiments prove the effectiveness and robustness of the model in the segmentation of complex curvature and small blood vessels.