Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network.
Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capabili...
| Publicado en: | BioMed Research International pp. 1 - 19 |
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| Autores principales: | , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
5/26/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150522864&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150522864 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 5/26/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 150522864 150522864 150522864 10.1155/2021/5561125 150522864 ppf: 1 ppct: 18 formats: fmt: @attributes: type: P tig: atl: Multichannel Retinal Blood Vessel Segmentation Based on the Combination of Matched Filter and U-Net Network. aug: au: Ma, Yuliang Zhu, Zhenbin Dong, Zhekang Shen, Tao Sun, Mingxu Kong, Wanzeng affil: Institute of Intelligent Control and Robotics, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang, China sug: subj: Retina Blood Supply Blood Vessels Pathology Neural Networks (Computer) Human Algorithms Coding Descriptive Statistics ab: Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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