Automatic identification and truncation of boundary outlets in complex imaging-derived biomedical geometries.
Efficient and accurate reconstruction of imaging-derived geometries and subsequent quality mesh generation are enabling technologies for both clinical and research simulations. A challenging part of this process is the introduction of computable, orthogonal boundary patches, namely, the outlets, int...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 47; no. 9; pp. 989 - 1000 |
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| Autores principales: | , , , , , , , |
| Formato: | diagnostic images pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Sep2009
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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=104907036&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104907036 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2009 vid: 47 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104907036 NLM19526263 2010382761 10.1007/s11517-009-0501-9 NLM19526263 PMC2734875 104907036 ppf: 989 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Automatic identification and truncation of boundary outlets in complex imaging-derived biomedical geometries. aug: au: Jiao X Einstein DR Dyedov V Carson JP Jiao, Xiangmin Einstein, Daniel R Dyedov, Vladimir Carson, James P affil: Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA sug: subj: Image Processing, Computer Assisted Radiographic Image Enhancement Algorithms Automation ab: Efficient and accurate reconstruction of imaging-derived geometries and subsequent quality mesh generation are enabling technologies for both clinical and research simulations. A challenging part of this process is the introduction of computable, orthogonal boundary patches, namely, the outlets, into treed structures, such as vasculature, arterial or airway trees. We present efficient and robust algorithms for automatically identifying and truncating the outlets for complex geometries. Our approach is based on a conceptual decomposition of objects into tips, segments, and branches, where the tips determine the outlets. We define the tips by introducing a novel concept called the average interior center of curvature and identify the tips that are stable and noise resistant. We compute well-defined orthogonal planes, which truncate the tips into outlets. The rims of the outlets are connected into curves, and the outlets are then closed using Delaunay triangulation. We illustrate the effectiveness and robustness of our approach with a variety of complex lung and coronary artery geometries. pubtype: Academic Journal doctype: diagnostic images pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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