Precision of Cephalometric Landmark Identification: 3D vs. 2D

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Precision of Cephalometric Landmark Identification

................................................................................................................... iii DEDICATION .....................................................................................................................v ACKNOWLEDGEMENTS ............................................................................................... vi LIST OF TABLES ...................

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Precision of cephalometric landmark identification: cone-beam computed tomography vs conventional cephalometric views.

INTRODUCTION In this study, we compared the precision of landmark identification using displays of multi-planar cone-beam computed tomographic (CBCT) volumes and conventional lateral cephalograms (Ceph). METHODS Twenty presurgical orthodontic patients were radiographed with conventional Ceph and CBCT techniques. Five observers plotted 24 landmarks using computer displays of multi-planer recon...

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Evaluation of cephalometric landmark identification on CBCT multiplanar and 3D reconstructions.

OBJECTIVE To evaluate the reliability of three-dimensional (3D) landmark identification in cone-beam computed tomography (CBCT) using two different visualization techniques. MATERIALS AND METHODS Twelve CBCT images were randomly selected. Three observers independently repeated three times the identification of 30 landmarks using 3D reconstructions and 28 landmarks using multiplanar views. The...

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Personalized 3D-Aided 2D Facial Landmark Localization

Facial landmark detection in images obtained under varying acquisition conditions is a challenging problem. In this paper, we present a personalized landmark localization method that leverages information available from 2D/3D gallery data. To realize a robust correspondence between gallery and probe key points, we present several innovative solutions, including: (i) a hierarchical DAISY descrip...

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Robust cephalometric landmark identification using support vector machines

A robust and accurate image recognizer for cephalometric landmarking is presented. The recognizer uses Support Vector Machine (SVM) to model discrimination boundaries between different landmarks and also between the background frames. Large Margin Classification with non-linear kernels allows to extract relevant details from the landmarks, approaching human expert levels of recognition. In conj...

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ژورنال

عنوان ژورنال: Oral Surgery, Oral Medicine, Oral Pathology, Oral Radiology, and Endodontology

سال: 2009

ISSN: 1079-2104

DOI: 10.1016/j.tripleo.2008.12.033