Please use this identifier to cite or link to this item: https://doi.org/10.1007/s00138-009-0191-1
DC FieldValue
dc.titleOptimal occlusion of teeth using planar structure information
dc.contributor.authorHiew, L.T.
dc.contributor.authorOng, S.H.
dc.contributor.authorFoong, K.W.C.
dc.date.accessioned2014-06-17T03:00:21Z
dc.date.available2014-06-17T03:00:21Z
dc.date.issued2010-08
dc.identifier.citationHiew, L.T., Ong, S.H., Foong, K.W.C. (2010-08). Optimal occlusion of teeth using planar structure information. Machine Vision and Applications 21 (5) : 735-747. ScholarBank@NUS Repository. https://doi.org/10.1007/s00138-009-0191-1
dc.identifier.issn09328092
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/56936
dc.description.abstractIn orthodontics, occlusion is defined as the relationship between the upper and lower sets of teeth when the jaws are brought together. Understanding the nature of occlusion has important significance for the diagnosis and treatment of occlusal dysfunction and for planning reconstructive dentistry. The materials of study are 31 pairs of manually aligned dental study models. The upper and lower models are independently digitized using a laser surface scanner. Occlusion can be recovered by detecting and aligning a set of planes on the models. We describe a two-step procedure for determining the occlusal relationship using digitized dental models. The first step is a coarse alignment using four planar structures that are detected by K-means clustering, followed by principal component analysis. The second step is a refinement process using a variant of the iterative closest point technique. The quantitative results show that the algorithm is accurate, with an average measurement discrepancy of 0.74 mm between the physical and virtual models. © Springer-Verlag 2009.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1007/s00138-009-0191-1
dc.sourceScopus
dc.subjectAlignment
dc.subjectDental
dc.subjectK-means clustering
dc.subjectOcclusion
dc.subjectPCA
dc.typeArticle
dc.contributor.departmentPREVENTIVE DENTISTRY
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1007/s00138-009-0191-1
dc.description.sourcetitleMachine Vision and Applications
dc.description.volume21
dc.description.issue5
dc.description.page735-747
dc.description.codenMVAPE
dc.identifier.isiut000280249300013
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