Creation of standardized geometric model of the human upper respiratory airway

Sun, Daoyuan; Xiangjun, Shao; Li, Fusheng; Li, Mintang; Xu, Xin

doi:10.1109/icmipe.2013.6864499

Cited by 1 publication

(2 citation statements)

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“…In literature, different techniques (e.g. level sets, Fourier descriptors) have already been used to create a shape model of the nose [18][19][20][21][22]. In Liu et al [20] and Sun et al [22] image alignment and image processing of cross-sections are used to create a standardized geometrical model.…”

Section: Introductionmentioning

confidence: 99%

“…level sets, Fourier descriptors) have already been used to create a shape model of the nose [18][19][20][21][22]. In Liu et al [20] and Sun et al [22] image alignment and image processing of cross-sections are used to create a standardized geometrical model. In the latter work, a full upper respiratory airway model is created, containing less details of the nasal complex.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.

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