Statistical shape modelling of the human mandible: 3D shape predictions based on external morphometric features

Abstract

One of the main limitations in subject-centred design approach is represented by getting 3D models of the region of interest. Indeed, 3D reconstruction from imaging data (i.e., computed tomography scans) is expensive and exposes the subject to high radiation doses. Statistical Shape Models (SSMs) are mathematical models able to describe the variability associated to a population and allow predicting new shapes tuning model parameters. These parameters almost never have a physical meaning and so they cannot be directly related to morphometric features. In this study a gender-combined SSM model of the human mandible was setup, using Generalised Procrustes Analysis and Principal Component Analysis on a dataset of fifty mandibles. Twelve morphometric features, able to characterise the mandibular bone and readily collectable during external examinations, were recorded and correlated to SSM parameters by a multiple linear regression approach. Then a cross-validation procedure was performed on a control set to determine the combination of features able to minimise the average deviation between real and predicted shapes. Compactness of the SSM and main modes of deformations have been investigated and results consistent with previous works involving a higher number of shapes were found. A combination of five features was proved to characterise predicted shapes minimising the average error. As completion of the work, a male SSM was developed and performances compared with those of the combined SSM. The features-based model here proposed could represent a useful and easy-to-use tool for the generation of 3D customised models within a virtual interactive design environment.