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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 (Nā€Š=ā€Š597; 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 landmarks placed manually on a surface-based                                representation of the population-based atlas.Landmarks are defined in red.
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pone-0020241-g002: Facial landmarks placed manually on a surface-based representation of the population-based atlas.Landmarks are defined in red.

Mentions: In order to create a point distribution, we use methods employed previously in model-based segmentation techniques in neuroimaging studies. In these types of methodologies [40], [44], anatomical landmarks are defined on an individual model and then warped back to individual subjects using a nonlinear transformation. In this case, two of the authors familiar with craniofacial anatomy (MMC and RA) placed landmarks on a surface- and voxel-representation of the nonlinear model defined in the previous section (see Figure 2). Our methods improve on this technique as landmarks need to be defined only on the model and are automatically customized to each individual face using the inverse of each individual's nonlinear transformation estimated previously (See 2.3.1). Note that this transformation brings the landmarks to the space corresponding to the linear (12-parameter) registration; as such, global differences in head size have been removed. This is analogous to the Procrustes method of superposition used in previous studies [12], [13].


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 landmarks placed manually on a surface-based                                representation of the population-based atlas.Landmarks are defined in red.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0020241-g002: Facial landmarks placed manually on a surface-based representation of the population-based atlas.Landmarks are defined in red.
Mentions: In order to create a point distribution, we use methods employed previously in model-based segmentation techniques in neuroimaging studies. In these types of methodologies [40], [44], anatomical landmarks are defined on an individual model and then warped back to individual subjects using a nonlinear transformation. In this case, two of the authors familiar with craniofacial anatomy (MMC and RA) placed landmarks on a surface- and voxel-representation of the nonlinear model defined in the previous section (see Figure 2). Our methods improve on this technique as landmarks need to be defined only on the model and are automatically customized to each individual face using the inverse of each individual's nonlinear transformation estimated previously (See 2.3.1). Note that this transformation brings the landmarks to the space corresponding to the linear (12-parameter) registration; as such, global differences in head size have been removed. This is analogous to the Procrustes method of superposition used in previous studies [12], [13].

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 (Nā€Š=ā€Š597; 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