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J. Imaging 2017, 3(4), 55;

Modelling of Orthogonal Craniofacial Profiles

Department of Computer Science, University of York, Heslington, York YO10 5GH, UK
Alder Hey Children’s Hospital, Liverpool L12 2AP, UK
This paper is an extended version of our paper published in Annual Conference on Medical Image Understanding and Analysis, Edinburgh, UK, 11–13 July 2017.
Author to whom correspondence should be addressed.
Received: 20 October 2017 / Revised: 18 November 2017 / Accepted: 23 November 2017 / Published: 30 November 2017
(This article belongs to the Special Issue Selected Papers from “MIUA 2017”)
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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. View Full-Text
Keywords: morphable model; shape modelling; craniofacial; craniosynostosis morphable model; shape modelling; craniofacial; craniosynostosis

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Dai, H.; Pears, N.; Duncan, C. Modelling of Orthogonal Craniofacial Profiles. J. Imaging 2017, 3, 55.

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