A morphable model of the human head validating the outcomes of an age-dependent scaphocephaly correction

Robertson, Ben; Dai, Hang; Pears, Nick; Duncan, Christian A.

doi:10.1016/j.ijom.2017.02.248

Cited by 5 publications

(5 citation statements)

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“…Craniosynostosis is a skull condition whereby, during skull growth and development, the sutures prematurely fuse, leading to both an abnormally-shaped cranium and increased intracranial pressure [34]. We present a case study of 25 craniosynostosis patients (all boys), 14 of which have undergone one type of corrective procedure called Barrel Staving (BS) and the other 11, another corrective procedure called Total Calvarial Remodelling (TCR).…”

Section: Craniosynostosis Intervention Outcome Evaluationmentioning

confidence: 99%

Modelling of Orthogonal Craniofacial Profiles

2017

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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.

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Section: Craniosynostosis Intervention Outcome Evaluationmentioning

confidence: 99%

Modelling of Orthogonal Craniofacial Profiles

2017

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“…We evaluated the proposed registration framework using three datasets: Headspace [6,7,8], BU3D (neutral expression) [9] and LSFM-synthetic. The latter two have ground-truth information.…”

Section: Discussionmentioning

confidence: 99%

“…Our proposed pipeline is evaluated qualitatively and quantitatively on three human face/head datasets: Headspace [6,7,8], BU3D [9] and LSFM-synthetic [10], and is compared with several other methods. Note that this latter dataset are 100 faces generated randomly from the Large Scale Face Model [10] where samples lie within ±3SDs.The pipelineel achieves state-of-the-art performance.…”

Section: Templatementioning

confidence: 99%

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Non-rigid 3D Shape Registration Using an Adaptive Template

Dai

Pears

Smith

2019

Lecture Notes in Computer Science

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We present a new fully-automatic non-rigid 3D shape registration (morphing) framework comprising (1) a new 3D landmarking and pose normalisation method; (2) an adaptive shape template method to improve the convergence of registration algorithms and achieve a better final shape correspondence and (3) a new iterative registration method that combines Iterative Closest Points with Coherent Point Drift (CPD) to achieve a more stable and accurate correspondence establishment than standard CPD. We call this new morphing approach Iterative Coherent Point Drift (ICPD). Our proposed framework is evaluated qualitatively and quantitatively on three datasets: Headspace, BU3D and a synthetic LSFM dataset, and is compared with several other methods. The proposed framework is shown to give state-of-the-art performance.

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“…We evaluate several deformation methods qualitatively and quantitatively using 1212 3D images in the Headspace dataset [29], [31], [32], which will be made public. The following subsections describe (A) qualitative and quantitative tangential sliding evaluation, (B) robustness to noise, and (C) gender and age classification performance using SVMs.…”

Section: Discussionmentioning

confidence: 99%

Symmetric Shape Morphing for 3D Face and Head Modelling

Dai

Pears

Smith

et al. 2018

2018 13th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2018)

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We propose a shape template morphing approach suitable for any class of shapes that exhibits approximate reflective symmetry over some plane. The human face and full head are examples. A shape morphing algorithm that constrains all morphs to be symmetric is a form of deformation regulation. This mitigates undesirable effects seen in standard morphing algorithms that are not symmetry-aware, such as tangential sliding. Our method builds on the Coherent Point Drift (CPD) algorithm and is called Symmetry-aware CPD (SA-CPD). Global symmetric deformations are obtained by removal of asymmetric shear from CPD's global affine transformations. Symmetrised local deformations are then used to improve the symmetric template fit. These symmetric deformations are followed by Laplace-Beltrami regularized projection which allows the shape template to fit to any asymmetries in the raw shape data. The pipeline facilitates construction of statistical models that are readily factored into symmetrical and asymmetrical components. Evaluations demonstrate that SA-CPD mitigates tangential sliding problem in CPD and outperforms other competing shape morphing methods, in some cases substantially. 3D morphable models are constructed from over 1200 full head scans, and we evaluate the constructed models in terms of age and gender classification. The best performance, in the context of SVM classification, is achieved using the proposed SA-CPD deformation algorithm.

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A morphable model of the human head validating the outcomes of an age-dependent scaphocephaly correction

Cited by 5 publications

References 0 publications

Modelling of Orthogonal Craniofacial Profiles

Modelling of Orthogonal Craniofacial Profiles

Non-rigid 3D Shape Registration Using an Adaptive Template

Symmetric Shape Morphing for 3D Face and Head Modelling

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