An automatic evaluation method for retinal image registration based on similar vessel structure matching.
Registration of retinal images is significant for clinical diagnosis. Numerous methods have been proposed to evaluate registration performance. The available evaluation methods can work well in normal image pairs, but fair evaluation cannot be obtained for image pairs with anatomical changes. We pro...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 1; pp. 117 - 130 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jan2020
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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=141101367&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141101367 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2020 vid: 58 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141101367 141101367 NLM31754981 10.1007/s11517-019-02080-0 NLM31754981 141101367 ppf: 117 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An automatic evaluation method for retinal image registration based on similar vessel structure matching. aug: au: Shu, Yifan Feng, Yunlong Wu, Guannan Kang, Jieliang Li, Huiqi affil: School of Information and Electronics, Beijing Institute of Technology, No. 5 South Zhong Guan Cun Street, Haidian District, 100081, Beijing, China sug: subj: Image Processing, Computer Assisted Retina Computer Simulation Algorithms Databases Automation Scales ab: Registration of retinal images is significant for clinical diagnosis. Numerous methods have been proposed to evaluate registration performance. The available evaluation methods can work well in normal image pairs, but fair evaluation cannot be obtained for image pairs with anatomical changes. We propose an automatic method to quantitatively assess the registration of retinal images based on the extraction of similar vessel structures and modified Hausdorff distance. Firstly, vessel detection and skeletonization are performed to detect the vascular centerline. Secondly, the vessel segments having similar structures in the image pair are selected for assessment of registration. The bifurcation and terminal points are determined from the vascular centerline. Then, the Hungarian matching algorithm with a pruning process is employed to match the bifurcation and terminal points to detect similar vessel segments. Finally, a modified Hausdorff distance is employed to evaluate the performance of registration. Our experimental results show that the Pearson product-moment correlation coefficient can reach 0.76 and 0.63 in test set of normal image pairs and image pairs with anomalies respectively, which outperforms other methods. An accurate evaluation can not only compare the performance of different registration methods but also can facilitate the clinical diagnosis by screening out the inaccurate registration. Graphical abstract . pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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