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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Retinal Vessels Segmentation Based on Multi-scale Hybrid Convolutional Network


    Weitere Titelangaben:

    Smart Innovation, Systems and Technologies


    Beteiligte:
    Wu, Tsu-Yang (Herausgeber:in) / Ni, Shaoquan (Herausgeber:in) / Chu, Shu-Chuan (Herausgeber:in) / Chen, Chi-Hua (Herausgeber:in) / Favorskaya, Margarita (Herausgeber:in) / Li, Rui (Autor:in) / Li, Zuoyong (Autor:in) / Cao, Xinrong (Autor:in) / Teng, Shenghua (Autor:in)


    Erscheinungsdatum :

    2021-11-30


    Format / Umfang :

    9 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Retinal Vessels Segmentation Based on Multi-scale Hybrid Convolutional Network

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


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

    ZHANG MEIYUE / WANG SENZHANG / MIAO HAO et al. | Europäisches Patentamt | 2021

    Freier Zugriff

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

    JIN SHENG / ZHOU MENGTAO / JIANG YANG et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    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

    Freier Zugriff