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The accurate segmentation of the teeth from the digitized representation of a dental study model is an important component in computer-based algorithms for orthodontic feature detection and measurement and in the simulation of orthodontic procedures such as tooth rearrangement. This paper presents an automated method for tooth segmentation from the three-dimensional (3-D) digitized image captured by a laser scanner. We avoid the complexity of directly processing 3-D mesh data by proposing the innovative idea of detecting features on two range images computed from the 3-D image. The dental arch is first obtained from the plan-view range image. Using the arch as the reference, a panoramic range image of the dental model can be computed. The interstices between the teeth are detected separately in the two range images, and results from both views are combined for a determination of interstice locations and orientations. Finally, the teeth are separated from the gums by delineating the gum margin. The algorithm was tested on 34 dental models representing a variety of malocclusions and was found to be robust and accurate. [ABSTRACT FROM AUTHOR]

Introduction: During orthodontic bonding procedures, excess adhesive is invariably left on the tooth surface at the interface between the bracket and the enamel junction; it is called excess adhesive flash (EAF). We comparatively evaluated the biofilm formation of Streptococcus mutans on EAF produced by 2 adhesives and examined the therapeutic efficacy of xylitol on S mutans formed on EAF., Methods: First, we investigated the biofilm formation of S mutans on 3 orthodontic bracket types: stainless steel preadjusted edgewise, ceramic preadjusted edgewise, and stainless steel self-ligating. Subsequently, tooth-colored Transbond XT (3M Unitek, Monrovia, Calif) and green Grengloo (Ormco, Glendora, Calif) adhesives were used for bonding ceramic brackets to extracted teeth. S mutans biofilms on EAF produced by the adhesives were studied using the crystal violet assay and scanning electron microscopy. Surface roughness and surface energy of the EAF were examined. The therapeutic efficacies of different concentrations of xylitol were tested on S mutans biofilms., Results: Significantly higher biofilms were formed on the ceramic preadjusted edgewise brackets (P = 0.003). Transbond XT had significantly higher S mutans biofilms compared with Grengloo surfaces (P = 0.007). There was no significant difference in surface roughness between Transbond XT and Grengloo surfaces (P >0.05). Surface energy of Transbond XT had a considerably smaller contact angle than did Grengloo, suggesting that Transbond XT is a more hydrophilic material. Xylitol at low concentrations had no significant effect on the reduction of S mutans biofilms on orthodontic adhesives (P = 0.016)., Conclusions: Transbond XT orthodontic adhesive resulted in more S mutans biofilm compared with Grengloo adhesive on ceramic brackets. Surface energy seemed to play a more important role than surface roughness for the formation of S mutans biofilm on EAF. Xylitol does not appear to have a therapeutic effect on mature S mutans biofilm., (Copyright © 2017 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.)

Introduction: The purpose of this pilot study was to use cone-beam computed tomography (CBCT) to determine the volumes of the maxilla and the mandible in subjects with skeletal Class I, Class II, and Class III malocclusions. Hypothesis 1 was that the volume (size) of a skeletal Class II maxilla is larger than those of Class I and Class III. Hypothesis 2 was that the volume of a skeletal Class III mandible is larger than those of Class I and Class II., Methods: Thirty women patients were classified into 3 groups according to their skeletal pattern: skeletal Class I (0 degrees < or =ANB <6 degrees ), Class II (ANB > or =6 degrees ) and Class III (ANB <0 degrees ). The volumes of the maxilla and the mandible were measured with CBCT. CB MercuRay (Hitachi Medico, Tokyo, Japan) and CB works software (CyberMed, Seoul, Korea) were used to process the images., Results: There was a trend that skeletal Class III subjects might have significantly greater mandibular volume compared with Class II subjects (P = 0.089). The ratios of maxilla-to-mandible volumes between the skeletal Class II and Class III groups were significantly different (P = 0.005). Differences were observed in the ratios of maxillary and mandibular volumes across the 3 groups., Conclusions: Hypotheses 1 and 2 were rejected; there was no trend for Class III subjects to have larger mandibles (P = 0.089) compared with Class II subjects. The ratio of the maxilla and mandible volumes in skeletal Class III subjects was significantly larger (P = 0.005) compared with Class II subjects.

Properly aligned teeth and a beautiful smile are the twin goals of orthodontic treatment. Unfortunately, a change in the smile arc is sometimes an unintended consequence of proper alignment. We used 3-dimensional dental models and visualization techniques, including curve-fitting and image-processing algorithms, to analyze smile arcs with respect to different parameters. The results show that smile consonance depends greatly on the conversational distance and the angle of elevation between the viewer and the smile.

Computed tomography (CT) is increasingly employed in orthodontic treatment. One of the most successful applications is in dental implantology, in which an artificial root is surgically inserted into the jawbone to provide anchorage for a dental prosthesis. For successful implant surgery, it is crucial to locate internal structures such as the inferior alveolar nerve canal (IAC). This paper presents a computerized technique for extracting the IAC. To facilitate the extraction, we first generate panoramic CT images (panoramics) by reformatting the original CT images. The panoramics are a series of cross-sectional images along curved planes through the mandible (lower jawbone). Hollow canals are subsequently detected by analyzing the voxel intensities and 3-D gradient orientations in the panoramics. The axis of the IAC is then traced out by a novel 3-D line-tracking technique. The method is effective for extracting the IAC despite the open structure of the surrounding bone.

The accurate segmentation of the teeth from the digitized representation of a dental study model is an important component in computer-based algorithms for orthodontic feature detection and measurement and in the simulation of orthodontic procedures such as tooth rearrangement. This paper presents an automated method for tooth segmentation from the three-dimensional (3-D) digitized image captured by a laser scanner. We avoid the complexity of directly processing 3-D mesh data by proposing the innovative idea of detecting features on two range images computed from the 3-D image. The dental arch is first obtained from the plan-view range image. Using the arch as the reference, a panoramic range image of the dental model can be computed. The interstices between the teeth are detected separately in the two range images, and results from both views are combined for a determination of interstice locations and orientations. Finally, the teeth are separated from the gums by delineating the gum margin. The algorithm was tested on 34 dental models representing a variety of malocclusions and was found to be robust and accurate.