Facial morphology prediction after complete denture restoration based on principal component analysis

Abstract

In developing treatment plan before complete denture restoration, doctors need to help the patient regain chewing ability while considering facial shape reconstruction after the surgery. At present, facial deformation prediction depends on the subjective judgment and experience of doctors; thus, an accurate basis for scientific quantitative analysis is lacking. With the development of computer technology, this paper proposed new facial morphology prediction method based on principal component analysis. Firstly, the curvature feature template with few feature points is constructed to replace the deformed areas of facial models. Secondly, the principal component analysis method is used to construct an elastic deformation prediction model for complex skin tissue. Finally, the Laplacian deformation technology is used to reconstruct the facial model and to obtain an intuitive digital 3D model. This method can adjust the facial deformation amplitude interactively by controlling shape parameters and predict the effect in consideration of different doctors' varied needs and habits. The experiments show that this method can predict the facial models interactively and the average deviation between the prediction models and the post-treatment facial models is between −2.102 and 2.102 mm by adjusting the shape parameters.