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Automated Analysis of Craniofacial Morphology Using Magnetic Resonance Images
Quantitative analysis of craniofacial morphology is of interest to scholars working in a wide variety of disciplines, such as anthropology, developmental biology, and medicine. T1-weighted (anatomical) magnetic resonance images (MRI) provide excellent contrast between soft tissues. Given its three-d...
Autores principales: | , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3105012/ https://www.ncbi.nlm.nih.gov/pubmed/21655288 http://dx.doi.org/10.1371/journal.pone.0020241 |
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author | Chakravarty, M. Mallar Aleong, Rosanne Leonard, Gabriel Perron, Michel Pike, G. Bruce Richer, Louis Veillette, Suzanne Pausova, Zdenka Paus, Tomáš |
author_facet | Chakravarty, M. Mallar Aleong, Rosanne Leonard, Gabriel Perron, Michel Pike, G. Bruce Richer, Louis Veillette, Suzanne Pausova, Zdenka Paus, Tomáš |
author_sort | Chakravarty, M. Mallar |
collection | PubMed |
description | Quantitative analysis of craniofacial morphology is of interest to scholars working in a wide variety of disciplines, such as anthropology, developmental biology, and medicine. T1-weighted (anatomical) magnetic resonance images (MRI) provide excellent contrast between soft tissues. Given its three-dimensional nature, MRI represents an ideal imaging modality for the analysis of craniofacial structure in living individuals. Here we describe how T1-weighted MR images, acquired to examine brain anatomy, can also be used to analyze facial features. Using a sample of typically developing adolescents from the Saguenay Youth Study (N = 597; 292 male, 305 female, ages: 12 to 18 years), we quantified inter-individual variations in craniofacial structure in two ways. First, we adapted existing nonlinear registration-based morphological techniques to generate iteratively a group-wise population average of craniofacial features. The nonlinear transformations were used to map the craniofacial structure of each individual to the population average. Using voxel-wise measures of expansion and contraction, we then examined the effects of sex and age on inter-individual variations in facial features. Second, we employed a landmark-based approach to quantify variations in face surfaces. This approach involves: (a) placing 56 landmarks (forehead, nose, lips, jaw-line, cheekbones, and eyes) on a surface representation of the MRI-based group average; (b) warping the landmarks to the individual faces using the inverse nonlinear transformation estimated for each person; and (3) using a principal components analysis (PCA) of the warped landmarks to identify facial features (i.e. clusters of landmarks) that vary in our sample in a correlated fashion. As with the voxel-wise analysis of the deformation fields, we examined the effects of sex and age on the PCA-derived spatial relationships between facial features. Both methods demonstrated significant sexual dimorphism in craniofacial structure in areas such as the chin, mandible, lips, and nose. |
format | Text |
id | pubmed-3105012 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-31050122011-06-08 Automated Analysis of Craniofacial Morphology Using Magnetic Resonance Images Chakravarty, M. Mallar Aleong, Rosanne Leonard, Gabriel Perron, Michel Pike, G. Bruce Richer, Louis Veillette, Suzanne Pausova, Zdenka Paus, Tomáš PLoS One Research Article Quantitative analysis of craniofacial morphology is of interest to scholars working in a wide variety of disciplines, such as anthropology, developmental biology, and medicine. T1-weighted (anatomical) magnetic resonance images (MRI) provide excellent contrast between soft tissues. Given its three-dimensional nature, MRI represents an ideal imaging modality for the analysis of craniofacial structure in living individuals. Here we describe how T1-weighted MR images, acquired to examine brain anatomy, can also be used to analyze facial features. Using a sample of typically developing adolescents from the Saguenay Youth Study (N = 597; 292 male, 305 female, ages: 12 to 18 years), we quantified inter-individual variations in craniofacial structure in two ways. First, we adapted existing nonlinear registration-based morphological techniques to generate iteratively a group-wise population average of craniofacial features. The nonlinear transformations were used to map the craniofacial structure of each individual to the population average. Using voxel-wise measures of expansion and contraction, we then examined the effects of sex and age on inter-individual variations in facial features. Second, we employed a landmark-based approach to quantify variations in face surfaces. This approach involves: (a) placing 56 landmarks (forehead, nose, lips, jaw-line, cheekbones, and eyes) on a surface representation of the MRI-based group average; (b) warping the landmarks to the individual faces using the inverse nonlinear transformation estimated for each person; and (3) using a principal components analysis (PCA) of the warped landmarks to identify facial features (i.e. clusters of landmarks) that vary in our sample in a correlated fashion. As with the voxel-wise analysis of the deformation fields, we examined the effects of sex and age on the PCA-derived spatial relationships between facial features. Both methods demonstrated significant sexual dimorphism in craniofacial structure in areas such as the chin, mandible, lips, and nose. Public Library of Science 2011-05-31 /pmc/articles/PMC3105012/ /pubmed/21655288 http://dx.doi.org/10.1371/journal.pone.0020241 Text en Chakravarty et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Chakravarty, M. Mallar Aleong, Rosanne Leonard, Gabriel Perron, Michel Pike, G. Bruce Richer, Louis Veillette, Suzanne Pausova, Zdenka Paus, Tomáš Automated Analysis of Craniofacial Morphology Using Magnetic Resonance Images |
title | Automated Analysis of Craniofacial Morphology Using Magnetic
Resonance Images |
title_full | Automated Analysis of Craniofacial Morphology Using Magnetic
Resonance Images |
title_fullStr | Automated Analysis of Craniofacial Morphology Using Magnetic
Resonance Images |
title_full_unstemmed | Automated Analysis of Craniofacial Morphology Using Magnetic
Resonance Images |
title_short | Automated Analysis of Craniofacial Morphology Using Magnetic
Resonance Images |
title_sort | automated analysis of craniofacial morphology using magnetic
resonance images |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3105012/ https://www.ncbi.nlm.nih.gov/pubmed/21655288 http://dx.doi.org/10.1371/journal.pone.0020241 |
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