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Automated Analysis of Craniofacial Morphology Using Magnetic Resonance Images

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  • M Mallar Chakravarty
  • Rosanne Aleong
  • Gabriel Leonard
  • Michel Perron
  • G Bruce Pike
  • Louis Richer
  • Suzanne Veillette
  • Zdenka Pausova
  • Tomáš Paus

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.

Suggested Citation

  • M Mallar Chakravarty & Rosanne Aleong & Gabriel Leonard & Michel Perron & G Bruce Pike & Louis Richer & Suzanne Veillette & Zdenka Pausova & Tomáš Paus, 2011. "Automated Analysis of Craniofacial Morphology Using Magnetic Resonance Images," PLOS ONE, Public Library of Science, vol. 6(5), pages 1-13, May.
  • Handle: RePEc:plo:pone00:0020241
    DOI: 10.1371/journal.pone.0020241
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