Non-rigid Point Set Registration with Application to Human Masticatory Muscle Deformation

Abstract

We first present a robust global and local mixture distance (GLMD) based non-rigid point set registration method which consists of an alternating two-step: correspondence estimation and transformation updating. The performance of the proposed method was tested for shape contour registrations and feature point matching in sequence images and real images, and also compared with six state-of-the-art methods where our method shows the best alignments in most scenarios. The proposed method was then applied to build a new framework to assess muscle deformation for exploring two practical problems in human masticatory system: masticatory muscle functional activity investigation, and biomechanical relationship between masticatory muscle activities and mandibular movements. Through the assessment of human masticatory muscle deformation, the framework provides an effective way to assess and visualize human masticatory muscle functional activity, and explain the biomechanical relationship between masticatory muscle activities and mandibular movements.