Parallel Architecture of Fully Convolved Neural Network for Retinal Vessel Segmentation.
Retinal blood vessel extraction is considered to be the indispensable action for the diagnostic purpose of many retinal diseases. In this work, a parallel fully convolved neural network–based architecture is proposed for the retinal blood vessel segmentation. Also, the network performance improvemen...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 1; pp. 168 - 181 |
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| Autores principales: | , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2020
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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=142164516&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142164516 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2020 vid: 33 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142164516 142164516 142164516 10.1007/s10278-019-00250-y 142164516 ppf: 168 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Parallel Architecture of Fully Convolved Neural Network for Retinal Vessel Segmentation. aug: au: .V, Sathananthavathi .G, Indumathi .A, Swetha Ranjani affil: Department of ECE, Mepco Schlenk Engineering College, 626005, Sivakasi, Tamilnadu, India sug: subj: Neural Networks (Computer) Retina Pathology Image Processing, Computer Assisted Methods Human Diagnosis, Computer Assisted Blood Vessels Databases Validity Sensitivity and Specificity Retinal Diseases Descriptive Statistics ab: Retinal blood vessel extraction is considered to be the indispensable action for the diagnostic purpose of many retinal diseases. In this work, a parallel fully convolved neural network–based architecture is proposed for the retinal blood vessel segmentation. Also, the network performance improvement is studied by applying different levels of preprocessed images. The proposed method is experimented on DRIVE (Digital Retinal Images for Vessel Extraction) and STARE (STructured Analysis of the Retina) which are the widely accepted public database for this research area. The proposed work attains high accuracy, sensitivity, and specificity of about 96.37%, 86.53%, and 98.18% respectively. Data independence is also proved by testing abnormal STARE images with DRIVE trained model. The proposed architecture shows better result in the vessel extraction irrespective of vessel thickness. The obtained results show that the proposed work outperforms most of the existing segmentation methodologies, and it can be implemented as the real time application tool since the entire work is carried out on CPU. The proposed work is executed with low-cost computation; at the same time, it takes less than 2 s per image for vessel extraction. 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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