High quality statistical shape modelling of the human nasal cavity and applications

Journal Article (2018)
Author(s)

William Keustermans (Universiteit Antwerpen)

T. Huysmans (Universiteit Antwerpen, TU Delft - Human Factors)

Femke Danckaers (Universiteit Antwerpen)

Andrzej Zarowski (GZA Sint-Augustinus Hospital)

Bert Schmelzer (ZNA Middelheim Hospital)

Jan Sijbers (Universiteit Antwerpen)

Joris J.J. Dirckx (Universiteit Antwerpen)

Research Group
Human Factors
Copyright
© 2018 William Keustermans, T. Huysmans, Femke Danckaers, Andrzej Zarowski, Bert Schmelzer, Jan Sijbers, J.J. Dirckx
DOI related publication
https://doi.org/10.1098/rsos.181558
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 William Keustermans, T. Huysmans, Femke Danckaers, Andrzej Zarowski, Bert Schmelzer, Jan Sijbers, J.J. Dirckx
Research Group
Human Factors
Issue number
12
Volume number
5
Pages (from-to)
1-17
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Abstract

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% 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.