D.J.J. Farnell, S. Richmond, J Galloway, AI Zhurov, P. Pirttiniemi, T. Heikkinen, V. Harila, H. Matthews, P. Claes, Multilevel principal components analysis of three-dimensional facial growth in adolescents, Computer Methods and Programs in Biomedicine, Volume 188, 2020, 105272, ISSN 0169-2607, https://doi.org/10.1016/j.cmpb.2019.105272
Multilevel principal components analysis of three-dimensional facial growth in adolescents
|Author:||Farnell, D.J.J.1; Richmond, S.1; Galloway, J.1;|
1School of Dentistry, Cardiff University, Heath Park, Cardiff CF14 4XY, United Kingdom
2Research Unit of Oral Health Sciences, Faculty of Medicine, University of Oulu, Oulu, Finland
3Medical Research Center Oulu (MRC Oulu), Oulu University Hospital, Oulu, Finland
4Medical Imaging Research Center, UZ Leuven, 3000 Leuven, Belgium
5Department of Human Genetics, KU Leuven, 3000 Leuven, Belgium
6OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, Leuven, Belgium
7Facial Sciences Research Group, Murdoch Children's Research Institute, Melbourne, Australia
8Department of Paediatrics, University of Melbourne, Melbourne, Australia
9Department of Electrical Engineering, ESAT/PSI, KU Leuven, 3000 Leuven, Belgium
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020051229507
|Publish Date:|| 2020-12-11
Background and objectives: The study of age-related facial shape changes across different populations and sexes requires new multivariate tools to disentangle different sources of variations present in 3D facial images. Here we wish to use a multivariate technique called multilevel principal components analysis (mPCA) to study three-dimensional facial growth in adolescents.
Methods: These facial shapes were captured for Welsh and Finnish subjects (both male and female) at multiple ages from 12 to 17 years old (i.e., repeated-measures data). 1000 “dense” 3D points were defined regularly for each shape by using a deformable template via “meshmonk” software. A three-level model was used here, namely: level 1 (sex/ethnicity); level 2, all “subject” variations excluding sex, ethnicity, and age; and level 3, age. The technicalities underpinning the mPCA method are presented in Appendices.
Results: Eigenvalues via mPCA predicted that: level 1 (ethnicity/sex) contained 7.9% of variation; level 2 contained 71.5%; and level 3 (age) contained 20.6%. The results for the eigenvalues via mPCA followed a similar pattern to those results of single-level PCA. Results for modes of variation made sense, where effects due to ethnicity, sex, and age were reflected in modes at appropriate levels of the model. Standardised scores at level 1 via mPCA showed much stronger differentiation between sex and ethnicity groups than results of single-level PCA. Results for standardised scores from both single-level PCA and mPCA at level 3 indicated that females had different average “trajectories” with respect to these scores than males, which suggests that facial shape matures in different ways for males and females. No strong evidence of differences in growth patterns between Finnish and Welsh subjects was observed.
Conclusions: mPCA results agree with existing research relating to the general process of facial changes in adolescents with respect to age quoted in the literature. They support previous evidence that suggests that males demonstrate larger changes and for a longer period of time compared to females, especially in the lower third of the face. These calculations are therefore an excellent initial test that multivariate multilevel methods such as mPCA can be used to describe such age-related changes for “dense” 3D point data.
Computer methods and programs in biomedicine
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
This investigation was supported by the KU Leuven, BOF (C14/15/081), NIH (1-RO1-DE027023) and the FWO Flanders (G078518N).
© 2019 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/.