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R Soc Open Sci. 2018 Dec 19;5(12):181558. doi: 10.1098/rsos.181558. eCollection 2018 Dec.

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

Royal Society open science

William Keustermans, Toon Huysmans, Femke Danckaers, Andrzej Zarowski, Bert Schmelzer, Jan Sijbers, Joris J J Dirckx

Affiliations

  1. Physics Department, University of Antwerp, Laboratory of Biophysics and Biomedical Physics, Groenenborgerlaan 171, 2020 Antwerp, Belgium.
  2. Faculty of Industrial Design Engineering, TU Delft, Landbergstraat 15, 2628 CE Delft, The Netherlands.
  3. Physics Department, University of Antwerp, Imec-Vision Lab, Edegemsesteenweg 200-240, 2610 Antwerp, Belgium.
  4. ENT Department, GZA Sint-Augustinus Hospital, Oosterveldlaan 24, 2610 Antwerp, Belgium.
  5. ENT Department, ZNA Middelheim Hospital, Lindendreef 1, 2020 Antwerp, Belgium.

PMID: 30662757 PMCID: PMC6304114 DOI: 10.1098/rsos.181558

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.

Keywords: age; cylindrical parametrization; gender; geometry average; morphology; nose

Conflict of interest statement

We declare we have no competing interests.

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