Deformation corrected workflow for maxillofacial prosthesis modelling

Abstract

The purpose of this article is to describe a deformation corrected workflow for maxillofacial prosthesis modelling based on the improved Laplace and iterative closest point–based iterative algorithms. For incomplete maxillofacial data with local deformed symmetrical features, the Laplace algorithm with rotation invariants was demonstrated that the operations can recover the local deformation while preserving the surface geometric detail; the M -estimation iterative closest point–based iterative algorithm integrated with the extended Gaussian image ensures the precision of the symmetry plane, making the outer point having almost no effect on the minimum process. The additional experiments also verified the ability of deformation corrected maxillofacial prosthesis modelling. Case study confirmed that this workflow is attractive and has potential to design the desired maxillofacial prosthesis for correcting the deformed oral soft tissue. The results of this study improve the quality of maxillofacial prostheses modelling. This technique will facilitate modelling of maxillofacial prostheses while helping the patients predict the effect before the prosthesis is manufactured. In addition, this deformation corrected workflow has great potential for improving the development of maxillofacial prosthesis modelling software.