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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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Related in: MedlinePlus

Facial morphometry changes related to age in females.Top row: Facial expansions related to age. Bottom row: Facial                            contractions related to age.
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pone-0020241-g005: Facial morphometry changes related to age in females.Top row: Facial expansions related to age. Bottom row: Facial contractions related to age.

Mentions: In female adolescents, there is very focal evidence of age-related changes in the facial structure (see Figure 5). Large age-related changes in the structure of the nose, the filtrum and the lips can be observed (DF = 286, p = 5.8×10−7, v = 1.7×106 mm3, peak t-value  = 7.0). Similarly, local expansions in the mandible and temple are also observed (DF = 286,p = 5.8×10−7, v = 3.8×105, t-value = −13.2). Age-related decreases in local volumes were found in the region of the scalp directly above the forehead (DF = 286, p = 5.8×10−7, v = 4.4×105 mm3, peak t-value  = −13.2). Other age-related decreases are also observed above the eyebrow ridge (DF = 286, p = 4.2×10−6, v = 3.5×105, peak t-value  = −7.4), left zygomatic arch (DF = 286, p = 0.00012, v = 3.3×104, peak t-value  = −6.1), right zygomatic arch (DF = 286, p = 0.00015, v = 3.3×104, peak t-value  = −6.4), and mandible (DF = 286, p = 0.0007, v = 3.8×104, peak t-value = −5.8).


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 females.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-g005: Facial morphometry changes related to age in females.Top row: Facial expansions related to age. Bottom row: Facial contractions related to age.
Mentions: In female adolescents, there is very focal evidence of age-related changes in the facial structure (see Figure 5). Large age-related changes in the structure of the nose, the filtrum and the lips can be observed (DF = 286, p = 5.8×10−7, v = 1.7×106 mm3, peak t-value  = 7.0). Similarly, local expansions in the mandible and temple are also observed (DF = 286,p = 5.8×10−7, v = 3.8×105, t-value = −13.2). Age-related decreases in local volumes were found in the region of the scalp directly above the forehead (DF = 286, p = 5.8×10−7, v = 4.4×105 mm3, peak t-value  = −13.2). Other age-related decreases are also observed above the eyebrow ridge (DF = 286, p = 4.2×10−6, v = 3.5×105, peak t-value  = −7.4), left zygomatic arch (DF = 286, p = 0.00012, v = 3.3×104, peak t-value  = −6.1), right zygomatic arch (DF = 286, p = 0.00015, v = 3.3×104, peak t-value  = −6.4), and mandible (DF = 286, p = 0.0007, v = 3.8×104, peak t-value = −5.8).

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