Unbiased groupwise registration for shape prediction of foot scans.
A graph-based groupwise shape registration algorithm for building statistical shape model (SSM) is proposed, which has been successfully applied to shape prediction of foot scans. Establishing unbiased and effective shape correspondences of large-scale data sets is extremely challenging, for the ina...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 9; pp. 1985 - 1999 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2019
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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=138201465&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138201465 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2019 vid: 57 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138201465 138201465 144036095 NLM31325102 10.1007/s11517-019-01992-1 NLM31325102 138201465 ppf: 1985 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Unbiased groupwise registration for shape prediction of foot scans. aug: au: Zhu, Jianjun Wang, Xiuxing Ma, Shaodong Fan, Jingfan Song, Shuang Ma, Xiao Ai, Danni Song, Hong Jiang, Yurong Wang, Yongtian Yang, Jian affil: Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, 100081, Beijing, China sug: subj: Imaging, Three-Dimensional Image Processing, Computer Assisted Foot Anatomy and Histology Algorithms Young Adult Female Models, Statistical Reproducibility of Results Middle Age Adolescence Male Adult Aged Questionnaires Middle Aged: 45-64 years Adolescent: 13-18 years Adult: 19-44 years Aged: 65+ years Female Male ab: A graph-based groupwise shape registration algorithm for building statistical shape model (SSM) is proposed, which has been successfully applied to shape prediction of foot scans. Establishing unbiased and effective shape correspondences of large-scale data sets is extremely challenging, for the inappropriate selection of initial mean shape and non-rigid registration of shape with large-scale deformation. To address these issues, first, we use a simplified graph to model the shape distribution in metric space and an edge-guided graph shrinkage to deform the shapes. Then, the groupwise registration is performed by iteratively performing the graph shrinkage until the shape converges. And, the correspondences of training shapes are obtained by propagating the converged shape to the original data along each shrinkage path. Compared with traditional forward and backward models of groupwise registration, the proposed method is data-driven without initial mean shape as input. Moreover, under the constraint of the established graph, the non-rigid registration can perform more accurately by restricting shape register to its neighbors. Based on the shape correspondence, the SSM of foot shapes is constructed and applied to shape prediction by taking the collected anthropometric information as predictor. Experiments demonstrate that the proposed method can obtain robust shape correspondences and SSM capability with respect to model generalization, specificity, and compactness. The application of shape prediction model shows an average prediction error lower than 1% for general foot size. Graphical abstract The graphical abstract of unbiased groupwise registration for foot prediction. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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