Semi-automatic registration of 3D orthodontics models from photographs

Abstract

International audienceIn orthodontics, a common practice used to diagnose and plan the treatment is the dental cast. After digitization by a CT-scan or a laser scanner, the obtained 3D surface models can feed orthodontics numerical tools for computer-aided diagnosis and treatment planning. One of the pre-processing critical steps is the 3D registration of dental arches to obtain the occlusion of these numerical models. For this task, we propose a vision based method to automatically compute the registration based on photos of patient mouth. From a set of matched singular points between two photos and the dental 3D models, the rigid transformation to apply to the mandible to be in contact with the maxillary may be computed by minimizing the reprojection errors. During a precedent study, we established the feasibility of this visual registration approach with a manual selection of singular points. This paper addresses the issue of automatic point detection. Based on a priori knowledge, histogram thresholding and edge detection are used to extract specific points in 2D images. Concurrently, curvatures information detects 3D corresponding points. To improve the quality of the final registration, we also introduce a combined optimization of the projection matrix with the 2D/3D point positions. These new developments are evaluated on real data by considering the reprojection errors and the deviation angles after registration in respect to the manual reference occlusion realized by a specialist. © (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only

3D dental image registration using exhaustive deformable models: a comparative study

“…Then, the transformation motion field V, can be estimated through Equation (15), where V ! ðX ; 0Þ5 0 !…”

“…13 Moreover, the registration of threedimensional (3D) dental data volumes was considered, using rigid-body semi-automatic or fully automatic techniques. 14,15 Similar to 2D imaging, the alignment of 3D dental data, such as CBCT volumes, allows dentistry experts to readily compare two or more imaging data sets from various viewing angles, thus aiding clinical assessment.…”