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Automated analysis of craniofacial morphology using magnetic resonance images.

Chakravarty MM, Aleong R, Leonard G, Perron M, Pike GB, Richer L, Veillette S, Pausova Z, Paus T - PLoS ONE (2011)

Bottom Line: Using voxel-wise measures of expansion and contraction, we then examined the effects of sex and age on inter-individual variations in facial features.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.

View Article: PubMed Central - PubMed

Affiliation: Rotman Research Institute, Baycrest, Toronto, Ontario, Canada. mchakravarty@rotman-baycrest.on.ca

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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Facial feature simulation (profile view) created by warping the                            average face using defined landmarks (see Fig. 5).PCs 1–5 are warped using 0.2, 0.4, 0.6, 0.8, and 1 of each PC                            score.
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pone-0020241-g007: Facial feature simulation (profile view) created by warping the average face using defined landmarks (see Fig. 5).PCs 1–5 are warped using 0.2, 0.4, 0.6, 0.8, and 1 of each PC score.

Mentions: Results of the simulation are shown in Figure 6 (the entire face) and Figure 7 (profile view). Each simulation demonstrates how each PC encodes a different relationship between facial features. Overall, PC1 demonstrates broadening of the forehead, chin, jaw, and nose; for PC2 the distance between all facial structures decreases and shows an increasing prominence of the forehead; PC3 is characterized by an enlarging brow line, broadening of the zygomatic arch and a less prominent jaw/chin; PC4 is characterized by a broadening of the chin, narrowing of the jaw and mouth, elongation of the nose, and a retreating jawline; and PC5 shows narrower cheekbones, fuller but narrower lips and a less prominent jawline. Note the exaggeration of facial features when the eigenvalue is fully sampled (last column in Figs 6 and 7), thereby providing a simulation of the relationship between different features within the population being studied.


Automated analysis of craniofacial morphology using magnetic resonance images.

Chakravarty MM, Aleong R, Leonard G, Perron M, Pike GB, Richer L, Veillette S, Pausova Z, Paus T - PLoS ONE (2011)

Facial feature simulation (profile view) created by warping the                            average face using defined landmarks (see Fig. 5).PCs 1–5 are warped using 0.2, 0.4, 0.6, 0.8, and 1 of each PC                            score.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3105012&req=5

pone-0020241-g007: Facial feature simulation (profile view) created by warping the average face using defined landmarks (see Fig. 5).PCs 1–5 are warped using 0.2, 0.4, 0.6, 0.8, and 1 of each PC score.
Mentions: Results of the simulation are shown in Figure 6 (the entire face) and Figure 7 (profile view). Each simulation demonstrates how each PC encodes a different relationship between facial features. Overall, PC1 demonstrates broadening of the forehead, chin, jaw, and nose; for PC2 the distance between all facial structures decreases and shows an increasing prominence of the forehead; PC3 is characterized by an enlarging brow line, broadening of the zygomatic arch and a less prominent jaw/chin; PC4 is characterized by a broadening of the chin, narrowing of the jaw and mouth, elongation of the nose, and a retreating jawline; and PC5 shows narrower cheekbones, fuller but narrower lips and a less prominent jawline. Note the exaggeration of facial features when the eigenvalue is fully sampled (last column in Figs 6 and 7), thereby providing a simulation of the relationship between different features within the population being studied.

Bottom Line: Using voxel-wise measures of expansion and contraction, we then examined the effects of sex and age on inter-individual variations in facial features.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.

View Article: PubMed Central - PubMed

Affiliation: Rotman Research Institute, Baycrest, Toronto, Ontario, Canada. mchakravarty@rotman-baycrest.on.ca

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.

Show MeSH
Related in: MedlinePlus