ArticlePloS one2011
Automated analysis of craniofacial morphology using magnetic resonance images.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Effects of chin supported maxillary protraction on symphsis morphology.Scientific reports · 2026Article
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- The effect of automated landmark identification on morphometric analyses.Journal of anatomy · 2019Article
- Age-related differences in olfactory cleft volume in adults: A computational volumetric study.The Laryngoscope · 2019Article
- Article
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- Multiple regression analysis of the craniofacial region of Chinese Han people using linear and angular measurements based on MRI.Forensic sciences research · 2017Article
- Hippocampal (subfield) volume and shape in relation to cognitive performance across the adult lifespan.Human brain mapping · 2015Article
- Error Rates in Users of Automatic Face Recognition Software.PloS one · 2015Article
- Morphometrics, 3D Imaging, and Craniofacial Development.Current topics in developmental biology · 2015Review
- Sexually dimorphic facial features vary according to level of autistic-like traits in the general population.Journal of neurodevelopmental disorders · 2015Article
- Mandibular symphysis morphology and dimensions in different anteroposterior jaw relationships.The Angle orthodontist · 2014Article
- FTO, obesity and the adolescent brain.Human molecular genetics · 2013Article
- A genome-wide association study identifies five loci influencing facial morphology in Europeans.PLoS genetics · 2012Article
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Authors and funding
9 authors.
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Abstract
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.
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