Tooth morphometry using quasi-conformal theory

Abstract

Shape analysis is important in anthropology, bioarchaeology and forensic science for interpreting useful information from human remains. In particular, teeth are morphologically stable and hence well-suited for shape analysis. In this work, we propose a framework for tooth morphometry using quasi-conformal theory. Landmark-matching Teichmüller maps are used for establishing a 1-1 correspondence between tooth surfaces with prescribed anatomical landmarks. Then, a quasi-conformal statistical shape analysis model based on the Teichmüller mapping results is proposed for building a tooth classification scheme. We deploy our framework on a dataset of human premolars to analyze the tooth shape variation among genders and ancestries. Experimental results show that our method achieves much higher classification accuracy with respect to both gender and ancestry when compared to the existing methods. Furthermore, our model reveals the underlying tooth shape difference between different genders and ancestries in terms of the local geometric distortion and curvatures.

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Cited by in corpus (11)

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• Adaptive area-preserving parameterization of open and closed anatomical surfaces
• Shape analysis via inconsistent surface registration
• Quasiconformal model with CNN features for large deformation image registration
• Quasi-conformal Geometry based Local Deformation Analysis of Lateral Cephalogram for Childhood OSA Classification
• Topology-Preserving 3D Image Segmentation Based On Hyperelastic Regularization
• Decomposition of Longitudinal Deformations via Beltrami Descriptors
• Data-driven quasiconformal morphodynamic flows