Retinal fundus image can reveal the information on the early symptoms of diabetes, hypertension, hyperlipidemia and other diseases. Accurate segmentation of retinal vessels can assist the detection and diagnosis of the related diseases. Due to the intricate characteristic information of retinal vessels images, traditional segmentation methods lead to inaccurate segmentation for small vessels and pathological segmentation errors. In this paper, we propose a new multi-scale hybrid convolution U-Net. Firstly, we take the hybrid convolution module by combining dilated convolution and standard convolution as the core structure for feature extraction to obtain more abundant semantic feature information, while expanding the receptive field. Then, we add a multi-scale fusion module to the network encoding and decoding connection part, which fuses the feature information of different layers to reduce the loss of information and enhance the representation ability of the network. We evaluate the performance of the proposed method on two public retinal datasets (DRIVE and CHASE_DB1). The results of quantitative and qualitative experiments show that the proposed model can improve good accuracy in retinal vessels segmentation.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Retinal Vessels Segmentation Based on Multi-scale Hybrid Convolutional Network


    Additional title:

    Smart Innovation, Systems and Technologies


    Contributors:


    Publication date :

    2021-11-30


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Retinal Vessels Segmentation Based on Multi-scale Hybrid Convolutional Network

    Li, Rui / Li, Zuoyong / Cao, Xinrong et al. | TIBKAT | 2022


    Driver identity identification method based on multi-source data and multi-scale convolutional network

    JIN SHENG / ZHOU MENGTAO / JIANG YANG et al. | European Patent Office | 2024

    Free access

    Multi-scale traffic flow prediction method based on graph convolutional neural network

    ZHANG MEIYUE / WANG SENZHANG / MIAO HAO et al. | European Patent Office | 2021

    Free access

    Travel Demand Prediction using Deep Multi-Scale Convolutional LSTM Network

    Chu, Kai Fung / Lam, Albert Y.S. / Li, Victor O.K. | IEEE | 2018


    Superpixel-Based Graph Convolutional Network for UAV Forest Fire Image Segmentation

    Yunjie Mu / Liyuan Ou / Wenjing Chen et al. | DOAJ | 2024

    Free access