Statistical reconstruction algorithm for restoring broken tooth surface based on occlusion spatial constraint

Abstract

Restoring the broken tooth surface which should coincide with the occlusal contact is a common essential problem in the modelling of dental restorations, such as crowns, inlays/onlays and veneers. Traditional modelling technologies have one obvious limitation: the size of standard template tooth library is limited, so it is hard to describe the diversity of natural tooth. To solve these problems, a novel statistical reconstruction algorithm for restoring broken tooth surface based on occlusion spatial constraint is presented. The correspondence between all sample crown models is established by means of generalized Procrustes analysis, whereby a number of corresponding landmarks are selected and the average crown model is calculated. The covariance matrix is established and principal component analysis is carried out which describing the differences between sample crown models and average crown model, so a mathematical representation of statistical crown morphology can be defined by a quantitative formulation. Finally, the broken tooth surface can be inferred with the statistical crown model by using occlusion spatial constraint points, which determine the deformable parameters of statistical deformable model. An experiment on the first lower molar demonstrates that the proposed method can describe enough anatomical morphology, the pose and anatomical features of the tooth will work well for chewing without need more complicated occlusion inspection.