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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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Results from the voxel-by-voxel analysis of deformation                            fields.Top Row: A surface rendered version of the population-based atlas. Middle                            Row: Parametric map projected onto the surface showing regions yielding                            statistically larger expansions in males in comparison to females.                            Bottom Row: Parametric map projected onto the surface showing regions                            yielding statistically larger expansions in females in comparison to                            males.
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pone-0020241-g003: Results from the voxel-by-voxel analysis of deformation fields.Top Row: A surface rendered version of the population-based atlas. Middle Row: Parametric map projected onto the surface showing regions yielding statistically larger expansions in males in comparison to females. Bottom Row: Parametric map projected onto the surface showing regions yielding statistically larger expansions in females in comparison to males.

Mentions: The results of the population-based model-building process demonstrate excellent alignment of the craniofacial structures. Figure 1 demonstrates axial and sagittal views from the model-building process. Each step in the process demonstrates increased structural contrast and anatomical resolution in comparison with the previous step. The initial population averages, generated by the 9-parameter and 12-parameter linear registrations, demonstrate large variability in the areas of the nose, chin, and lips. As expected, this variability is reduced considerably through each of the subsequent nonlinear steps. After visual inspection we determined that there were 28 overall registration failures during the image processing stages of the analyses. These subjects have been removed from the analysis. All further results are reported with these subjects removed. A surface-based representation (using a modified marching cubes-based extraction [47] of a segmentation of the final nonlinear model) is shown in the first row of Figure 3.


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)

Results from the voxel-by-voxel analysis of deformation                            fields.Top Row: A surface rendered version of the population-based atlas. Middle                            Row: Parametric map projected onto the surface showing regions yielding                            statistically larger expansions in males in comparison to females.                            Bottom Row: Parametric map projected onto the surface showing regions                            yielding statistically larger expansions in females in comparison to                            males.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0020241-g003: Results from the voxel-by-voxel analysis of deformation fields.Top Row: A surface rendered version of the population-based atlas. Middle Row: Parametric map projected onto the surface showing regions yielding statistically larger expansions in males in comparison to females. Bottom Row: Parametric map projected onto the surface showing regions yielding statistically larger expansions in females in comparison to males.
Mentions: The results of the population-based model-building process demonstrate excellent alignment of the craniofacial structures. Figure 1 demonstrates axial and sagittal views from the model-building process. Each step in the process demonstrates increased structural contrast and anatomical resolution in comparison with the previous step. The initial population averages, generated by the 9-parameter and 12-parameter linear registrations, demonstrate large variability in the areas of the nose, chin, and lips. As expected, this variability is reduced considerably through each of the subsequent nonlinear steps. After visual inspection we determined that there were 28 overall registration failures during the image processing stages of the analyses. These subjects have been removed from the analysis. All further results are reported with these subjects removed. A surface-based representation (using a modified marching cubes-based extraction [47] of a segmentation of the final nonlinear model) is shown in the first row of Figure 3.

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