Abstract
An important preprocess in computer-aided orthodontics is to segment teeth from the dental models accurately, which should involve manual interactions as few as possible. But fully automatic partition of all teeth is not a trivial task, since teeth occur in different shapes and their arrangements vary substantially from one individual to another. The difficulty is exacerbated when severe teeth malocclusion and crowding problems occur, which is a common occurrence in clinical cases. Most published methods in this area either are inaccurate or require lots of manual interactions. Motivated by the state-of-the-art general mesh segmentation methods that adopted the theory of harmonic field to detect partition boundaries, this paper proposes a novel, dental-targeted segmentation framework for dental meshes. With a specially designed weighting scheme and a strategy of a priori knowledge to guide the assignment of harmonic constraints, this method can identify teeth partition boundaries effectively. Extensive experiments and quantitative analysis demonstrate that the proposed method is able to partition high-quality teeth automatically with robustness and efficiency.
Highlights
In recent years, much effort has been spent in developing computerized systems for clinical and research applications in dentistry
Motivated by state-of-the-art general interactive mesh segmentation methods [5,6,7,8], we developed a convenient and efficient dental segmentation framework, which identifies tooth boundaries by a dental-targeted, harmonic field
We have tested our approach on 60 dental mesh models of varying complexity
Summary
Much effort has been spent in developing computerized systems for clinical and research applications in dentistry. Most computerized algorithms for orthodontic diagnostic and treatment require 3D dental mesh models, which are often needed to extract, move, and rearrange teeth for simulation of the treatment outcome. Teeth segmentation is an important step in many automated and semiautomated, computer-based dental software packages. Tooth segmentation on dental meshes remains a difficult task [1]. Dental meshes from patients often have teeth crowding problems when adjacent teeth are misaligned, making the interstices between them irregular and difficult to distinguish. Various tooth shapes make outlining tooth contours difficult. Artifacts resulting from scanning or model-making errors on commonly obtained clinical meshes make teeth segmentation more challenging
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