Computer aided planning for orthodontic treatment requires knowing occlusion of separately scanned dental casts. A visual guided registration is conducted starting by extracting corresponding features in both photographs and 3D scans. To achieve this, dental neck and occlusion surface are firstly extracted by image segmentation and 3D curvature analysis. Then, an iterative registration process is conducted during which feature positions are refined, guided by previously found anatomic edges. The occlusal edge image detection is improved by an original algorithm which follows Canny’s poorly detected edges using a priori knowledge of tooth shapes. Finally, the influence of feature extraction and position optimization is evaluated in terms of the quality of the induced registration. Best combination of feature detection and optimization leads to a positioning average error of 1.10 mm and 2.03°.
In 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.