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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 morphometry changes related to age in males.Top row: Facial expansions related to age. Bottom row: Facial                            contractions related to age.
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pone-0020241-g004: Facial morphometry changes related to age in males.Top row: Facial expansions related to age. Bottom row: Facial contractions related to age.

Mentions: Voxel-wise analysis of age-related changes in the deformation fields, carried out separately for male and female adolescents, yielded the following observations. In male adolescents (see Figure 4), there is an age-related broadening of the zygomatic arch, mandible, and bridge of the nose represented in one continuous cluster (DF = 287, p = 3.5×10−7, v = 1.95×107 mm3, peak t-value  = 13.0). Age-related decreases in the local volume are localized (in a single cluster) around the nose, lips, forehead, region of the eyebrow, bottom of the chin and in the temples, lateral to the forehead (DF = 287, p = 5.5×10−7, v = 1.3×107 mm3, peak t-value  = −21.9).


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 morphometry changes related to age in males.Top row: Facial expansions related to age. Bottom row: Facial                            contractions related to age.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0020241-g004: Facial morphometry changes related to age in males.Top row: Facial expansions related to age. Bottom row: Facial contractions related to age.
Mentions: Voxel-wise analysis of age-related changes in the deformation fields, carried out separately for male and female adolescents, yielded the following observations. In male adolescents (see Figure 4), there is an age-related broadening of the zygomatic arch, mandible, and bridge of the nose represented in one continuous cluster (DF = 287, p = 3.5×10−7, v = 1.95×107 mm3, peak t-value  = 13.0). Age-related decreases in the local volume are localized (in a single cluster) around the nose, lips, forehead, region of the eyebrow, bottom of the chin and in the temples, lateral to the forehead (DF = 287, p = 5.5×10−7, v = 1.3×107 mm3, peak t-value  = −21.9).

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