Modelling of Orthogonal Craniofacial Profiles. H Dai, N Pears, C Duncan.

Date: November 2017.
Source: Journal of Imaging 2017, 3(4), 55; doi:10.3390/jimaging3040055.
Abstract: We present a fully-automatic image processing pipeline to build a set of 2D morphable models of three craniofacial profiles from orthogonal viewpoints, side view, front view and top view, using a set of 3D head surface images. Subjects in this dataset wear a close-fitting latex cap to reveal the overall skull shape. Texture-based 3D pose normalization and facial landmarking are applied to extract the profiles from 3D raw scans. Fully-automatic profile annotation, subdivision and registration methods are used to establish dense correspondence among sagittal profiles. The collection of sagittal profiles in dense correspondence are scaled and aligned using Generalised Procrustes Analysis (GPA), before applying principal component analysis to generate a morphable model. Additionally, we propose a new alternative alignment called the Ellipse Centre Nasion (ECN) method. Our model is used in a case study of craniosynostosis intervention outcome evaluation, and the evaluation reveals that the proposed model achieves state-of-the-art results. We make publicly available both the morphable models and the profile dataset used to construct it.

Article: Modelling of Orthogonal Craniofacial Profiles.
Authors: Hang Dai, Nick Pears, and Christian Duncan. Alder Hey Children’s Hospital, Liverpool and Department of Computer Science, University of York, York, United Kingdom.