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Automatic Dent-landmark detection in 3-D CBCT dental volumes

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8 Author(s)
Erkang Cheng ; Center for Data Analytics & Biomedical Informatics, Computer & Information Science Department, Temple University, Philadelphia, PA, 19122 USA ; Jinwu Chen ; Jie Yang ; Huiyang Deng
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Orthodontic craniometric landmarks provide critical information in oral and maxillofacial imaging diagnosis and treatment planning. The Dent-landmark, defined as the odontoid process of the epistropheus, is one of the key landmarks to construct the midsagittal reference plane. In this paper, we propose a learning-based approach to automatically detect the Dent-landmark in the 3D cone-beam computed tomography (CBCT) dental data. Specifically, a detector is learned using the random forest with sampled context features. Furthermore, we use spacial prior to build a constrained search space other than use the full three dimensional space. The proposed method has been evaluated on a dataset containing 73 CBCT dental volumes and yields promising results.

Published in:

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Date of Conference:

Aug. 30 2011-Sept. 3 2011