Automatic Modelling of 3D Craniofacial Form

Nicholas Edwin Pears, Christian Duncan

Research output: Working paperPreprint


Three-dimensional models of craniofacial variation over the general population
are useful for assessing pre- and post-operative head shape when treating various craniofacial conditions, such as craniosynostosis. We present a new method of automatically building both sagittal pro le models and full 3D surface models of the human head using a range of techniques in 3D surface image analysis; in particular, automatic facial landmarking using supervised machine learning, global and local symmetry plane detection using a variant of trimmed iterative closest points, locally-affine template warping (for full 3D models) and a novel pose normalisation using robust iterative ellipse tting. The PCA-based models built using the new pose normalisation are more compact than those using Generalised Procrustes Analysis and we demonstrate their utility in a clinical case study.
Original languageEnglish
Number of pages57
Publication statusPublished - 22 Jan 2016


  • 3D shape modelling, Symmetry plane extraction, Automatic landmarking, 3D feature matching

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