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dc.contributor.author Keustermans, William
dc.contributor.author Huysmans, Toon
dc.contributor.author Danckaers, Femke
dc.contributor.author Zarowski, Andrzej
dc.contributor.author Schmelzer, Bert
dc.contributor.author Sijbers, Jan
dc.contributor.author Dirckx, Joris J. J.
dc.coverage.spatial Antwerp
dc.coverage.spatial Belgium
dc.date.accessioned 2018-12-06T17:31:10Z
dc.date.available 2018-12-06T17:31:10Z
dc.date.issued 2018-12-06
dc.identifier doi:10.5061/dryad.75fq000
dc.identifier.citation Keustermans W, Huysmans T, Danckaers F, Zarowski A, Schmelzer B, Sijbers J, Dirckx JJJ (2018) High quality statistical shape modelling of the human nasal cavity and applications. Royal Society Open Science 5(12): 181558.
dc.identifier.uri http://hdl.handle.net/10255/dryad.194239
dc.description The human nose is a complex organ that shows large morphological variations and has many important functions. However, the relation between shape and function is not yet fully understood. In this work, we present a high quality statistical shape model of the human nose based on clinical CT data of 46 patients. A technique based on cylindrical parametrization was used to create a correspondence between the nasal shapes of the population. Applying principal component analysis on these corresponded nasal cavities resulted in an average nasal geometry and geometrical variations, known as principal components, present in the population with a high precision. The analysis led to 46 principal components, which account for 95 percent of the total geometrical variation captured. These variations are first discussed qualitatively, and the effect on the average nasal shape of the first five principal components is visualized. Hereafter, by using this statistical shape model, two application examples that lead to quantitative data are shown: nasal shape in function of age and gender, and a morphometric analysis of different anatomical regions. Shape models, as the one presented here, can help to get a better understanding of nasal shape and variation, and their relationship with demographic data.
dc.relation.haspart doi:10.5061/dryad.75fq000/1
dc.relation.isreferencedby doi:10.1098/rsos.181558
dc.subject nose
dc.subject cylindrical parametrization
dc.subject geometry average
dc.subject morphology
dc.subject age
dc.subject gender
dc.title Data from: High quality statistical shape modelling of the human nasal cavity and applications
dc.type Article
dc.contributor.correspondingAuthor Keustermans, William
prism.publicationName Royal Society Open Science
dryad.dansTransferDate 2019-02-19T10:49:51.842+0000
dryad.dansEditIRI https://easy.dans.knaw.nl/sword2/container/db698e66-c0d2-457a-9ef4-c2472b6b659f
dryad.dansArchiveDate 2019-02-19T11:01:24.343+0000
dryad.dashTransferDate 2019-07-15T02:58:52.201+0000

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