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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Automated Digital Dental Articulation</title><author><name sortKey="Xia, James J" sort="Xia, James J" uniqKey="Xia J" first="James J." last="Xia">James J. Xia</name><affiliation><nlm:aff id="A1">The Methodist Hospital Research Institute, Houston, Texas, USA</nlm:aff></affiliation></author><author><name sortKey="Chang, Yu Bing" sort="Chang, Yu Bing" uniqKey="Chang Y" first="Yu-Bing" last="Chang">Yu-Bing Chang</name><affiliation><nlm:aff id="A1">The Methodist Hospital Research Institute, Houston, Texas, USA</nlm:aff></affiliation><affiliation><nlm:aff id="A2">Texas A&M University, College Station, Texas USA</nlm:aff></affiliation></author><author><name sortKey="Gateno, Jaime" sort="Gateno, Jaime" uniqKey="Gateno J" first="Jaime" last="Gateno">Jaime Gateno</name><affiliation><nlm:aff id="A1">The Methodist Hospital Research Institute, Houston, Texas, USA</nlm:aff></affiliation></author><author><name sortKey="Xiong, Zixiang" sort="Xiong, Zixiang" uniqKey="Xiong Z" first="Zixiang" last="Xiong">Zixiang Xiong</name><affiliation><nlm:aff id="A2">Texas A&M University, College Station, Texas USA</nlm:aff></affiliation></author><author><name sortKey="Zhou, Xiaobo" sort="Zhou, Xiaobo" uniqKey="Zhou X" first="Xiaobo" last="Zhou">Xiaobo Zhou</name><affiliation><nlm:aff id="A1">The Methodist Hospital Research Institute, Houston, Texas, USA</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">20879410</idno><idno type="pmc">5663470</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5663470</idno><idno type="RBID">PMC:5663470</idno><date when="2010">2010</date><idno type="wicri:Area/Pmc/Corpus">002644</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">002644</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Automated Digital Dental Articulation</title><author><name sortKey="Xia, James J" sort="Xia, James J" uniqKey="Xia J" first="James J." last="Xia">James J. Xia</name><affiliation><nlm:aff id="A1">The Methodist Hospital Research Institute, Houston, Texas, USA</nlm:aff></affiliation></author><author><name sortKey="Chang, Yu Bing" sort="Chang, Yu Bing" uniqKey="Chang Y" first="Yu-Bing" last="Chang">Yu-Bing Chang</name><affiliation><nlm:aff id="A1">The Methodist Hospital Research Institute, Houston, Texas, USA</nlm:aff></affiliation><affiliation><nlm:aff id="A2">Texas A&M University, College Station, Texas USA</nlm:aff></affiliation></author><author><name sortKey="Gateno, Jaime" sort="Gateno, Jaime" uniqKey="Gateno J" first="Jaime" last="Gateno">Jaime Gateno</name><affiliation><nlm:aff id="A1">The Methodist Hospital Research Institute, Houston, Texas, USA</nlm:aff></affiliation></author><author><name sortKey="Xiong, Zixiang" sort="Xiong, Zixiang" uniqKey="Xiong Z" first="Zixiang" last="Xiong">Zixiang Xiong</name><affiliation><nlm:aff id="A2">Texas A&M University, College Station, Texas USA</nlm:aff></affiliation></author><author><name sortKey="Zhou, Xiaobo" sort="Zhou, Xiaobo" uniqKey="Zhou X" first="Xiaobo" last="Zhou">Xiaobo Zhou</name><affiliation><nlm:aff id="A1">The Methodist Hospital Research Institute, Houston, Texas, USA</nlm:aff></affiliation></author></analytic><series><title level="j">Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention</title><imprint><date when="2010">2010</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P1">Articulating digital dental models is often inaccurate and very time-consuming. This paper presents an automated approach to efficiently articulate digital dental models to maximum intercuspation (MI). There are two steps in our method. The first step is to position the models to an initial position based on dental curves and a point matching algorithm. The second step is to finally position the models to the MI position based on our novel approach of using iterative surface-based minimum distance mapping with collision constraints. Finally, our method was validated using 12 sets of digital dental models. The results showed that using our method the digital dental models can be accurately and effectively articulated to MI position.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">101249582</journal-id><journal-id journal-id-type="pubmed-jr-id">32630</journal-id><journal-id journal-id-type="nlm-ta">Med Image Comput Comput Assist Interv</journal-id><journal-title-group><journal-title>Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention</journal-title></journal-title-group></journal-meta><article-meta><article-id pub-id-type="pmid">20879410</article-id><article-id pub-id-type="pmc">5663470</article-id><article-id pub-id-type="manuscript">NIHMS915095</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Automated Digital Dental Articulation</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Xia</surname><given-names>James J.</given-names></name><xref ref-type="aff" rid="A1">1</xref><pmc-comment>JXia@tmhs.org </pmc-comment>
</contrib><contrib contrib-type="author"><name><surname>Chang</surname><given-names>Yu-Bing</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="aff" rid="A2">2</xref><pmc-comment>yubingchang@tamu.edu </pmc-comment>
</contrib><contrib contrib-type="author"><name><surname>Gateno</surname><given-names>Jaime</given-names></name><xref ref-type="aff" rid="A1">1</xref><pmc-comment>JGateno@tmhs.org </pmc-comment>
</contrib><contrib contrib-type="author"><name><surname>Xiong</surname><given-names>Zixiang</given-names></name><xref ref-type="aff" rid="A2">2</xref><pmc-comment>XZhou@tmhs.org </pmc-comment>
</contrib><contrib contrib-type="author"><name><surname>Zhou</surname><given-names>Xiaobo</given-names></name><xref ref-type="aff" rid="A1">1</xref><pmc-comment>zx@ece.tmau.edu </pmc-comment>
</contrib></contrib-group><aff id="A1"><label>1</label>The Methodist Hospital Research Institute, Houston, Texas, USA</aff><aff id="A2"><label>2</label>Texas A&M University, College Station, Texas USA</aff><pub-date pub-type="nihms-submitted"><day>25</day><month>10</month><year>2017</year></pub-date><pub-date pub-type="ppub"><year>2010</year></pub-date><pub-date pub-type="pmc-release"><day>31</day><month>10</month><year>2017</year></pub-date><volume>13</volume><issue>Pt 3</issue><fpage>278</fpage><lpage>286</lpage><abstract><p id="P1">Articulating digital dental models is often inaccurate and very time-consuming. This paper presents an automated approach to efficiently articulate digital dental models to maximum intercuspation (MI). There are two steps in our method. The first step is to position the models to an initial position based on dental curves and a point matching algorithm. The second step is to finally position the models to the MI position based on our novel approach of using iterative surface-based minimum distance mapping with collision constraints. Finally, our method was validated using 12 sets of digital dental models. The results showed that using our method the digital dental models can be accurately and effectively articulated to MI position.</p></abstract><kwd-group><kwd>digital dental models</kwd><kwd>automated</kwd><kwd>digital dental articulation</kwd><kwd>collision avoidance</kwd></kwd-group></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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