Literature DB >> 27886708

Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible teeth.

Zeyang Xia1, Yangzhou Gan2, Lichao Chang3, Jing Xiong4, Qunfei Zhao5.   

Abstract

BACKGROUND AND
OBJECTIVE: Tooth segmentation from computed tomography (CT) images is a fundamental step in generating the three-dimensional models of tooth for computer-aided orthodontic treatment. Individual tooth segmentation from CT images scanned with contacts of maxillary and mandible teeth is especially challenging, and no method has been reported previously. This study aimed to develop a method for individual tooth segmentation from these images.
METHODS: Tooth contours of maxilla and mandible are first segmented from the volumetric CT images slice-by-slice. For each slice, a line is extracted using the Radon transform to separate neighboring teeth, and each tooth contour is then segmented by a level set model from the corresponding side of the line. Then, each maxillary tooth whose contours overlap with that of mandible ones is detected, and a mesh model is reconstructed from all the contours of these maxillary and mandible teeth with contour overlap. The reconstructed mesh model is segmented using threshold and fast marching watershed method to separate the touched maxillary and mandible teeth. Finally, the separated tooth models are restored to fill the holes to obtain complete tooth models. The proposed method was tested on CT images of ten subjects scanned with natural contacts of maxillary and mandible teeth.
RESULTS: For all the tested images, individual tooth regions are extracted successfully, and the segmentation accuracy and efficiency of the proposed method is promising.
CONCLUSIONS: The proposed method is effective to segment individual tooth from CT images scanned with contacts of maxillary and mandible teeth. Copyright Â
© 2016 Elsevier Ireland Ltd. All rights reserved.

Keywords:  Computed tomography images; Level set method; Mesh segmentation; Radon transform; Tooth segmentation

Mesh:

Year:  2016        PMID: 27886708     DOI: 10.1016/j.cmpb.2016.10.002

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


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