Ayumi Matsuoka1, Fumi Yoshioka2, Shogo Ozawa1, Jun Takebe1. 1. Department of Removable Prosthodontics, School of Dentistry, Aichi Gakuin University, Nagoya, Japan. 2. Department of Removable Prosthodontics, School of Dentistry, Aichi Gakuin University, Nagoya, Japan. Electronic address: fumi@dpc.agu.ac.jp.
Abstract
PURPOSE: It is essential to fabricate a best-fit three-dimensional (3D) facial prosthesis model capable of facial expressions. In order for the facial prosthesis to remain in position, especially around marginal areas subject to movement, a new method of making 3D facial expression models using time-series data allowing changes in facial expression by morphing technique was developed. METHODS: Seven normal subjects and seven patients with nasal defects or nasal deformities participated in this study. Three distinct facial expressions (i.e., a neutral expression, smiled, and open mouthed) were digitally acquired with a facial scanner. Prepared template models were transformed to homologous models, which can represent the form as shape data with the same number of point cloud data of the same topology referring to the scanning data. Finally, 3D facial expression models were completed by generating a morphing image based on two sets of homologous models, and the accuracy of the homologous models of all subjects was evaluated. RESULTS: 3D facial expression models of both normal subjects and patients with nasal defects were successfully generated. No significant differences in shape between the scanned models and homologous models were shown. CONCLUSIONS: The high accuracy of this 3D facial expression model in both normal subjects and patients suggests its use for fabricating facial prostheses.
PURPOSE: It is essential to fabricate a best-fit three-dimensional (3D) facial prosthesis model capable of facial expressions. In order for the facial prosthesis to remain in position, especially around marginal areas subject to movement, a new method of making 3D facial expression models using time-series data allowing changes in facial expression by morphing technique was developed. METHODS: Seven normal subjects and seven patients with nasal defects or nasal deformities participated in this study. Three distinct facial expressions (i.e., a neutral expression, smiled, and open mouthed) were digitally acquired with a facial scanner. Prepared template models were transformed to homologous models, which can represent the form as shape data with the same number of point cloud data of the same topology referring to the scanning data. Finally, 3D facial expression models were completed by generating a morphing image based on two sets of homologous models, and the accuracy of the homologous models of all subjects was evaluated. RESULTS: 3D facial expression models of both normal subjects and patients with nasal defects were successfully generated. No significant differences in shape between the scanned models and homologous models were shown. CONCLUSIONS: The high accuracy of this 3D facial expression model in both normal subjects and patients suggests its use for fabricating facial prostheses.