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The effect of threshold level on bone segmentation of cranial base structures from CT and CBCT images
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 4612672
Author(s) Friedli, Luca; Kloukos, Dimitrios; Kanavakis, Georgios; Halazonetis, Demetrios; Gkantidis, Nikolaos
Author(s) at UniBasel Kanavakis, Georgios
Year 2020
Title The effect of threshold level on bone segmentation of cranial base structures from CT and CBCT images
Journal Scientific Reports
Volume 10
Number 1
Pages / Article-Number 7361
Mesh terms Algorithms; Cone-Beam Computed Tomography, methods; Humans; Skull Base, anatomy & histology, diagnostic imaging; Spiral Cone-Beam Computed Tomography, methods
Abstract The use of a single grey intensity threshold is one of the most straightforward and widely used methods to segment cranial base surface models from a 3D radiographic volume. In this study we used thirty Cone Beam Computer Tomography (CBCT) scans from three different machines and ten CT scans of growing individuals to test the effect of thresholding on the subsequently produced anterior cranial base surface models. From each scan, six surface models were generated using a range of voxel intensity thresholds. The models were then superimposed on a manually selected reference surface model, using an iterative closest point algorithm. Multivariate tests showed significant effects of the machine type, threshold value, and superimposition on the spatial position and the form of the created models. For both, CT and CBCT machines, the distance between the models, as well as the variation within each threshold category, was consistently increasing with the magnitude of difference between thresholds. The present findings highlight the importance of accurate anterior cranial base segmentation for reliable assessment of craniofacial morphology through surface superimposition or similar methods that utilize this anatomical structure as reference.
Publisher Nature Publishing Group
ISSN/ISBN 2045-2322
URL http://www.ncbi.nlm.nih.gov/pmc/articles/pmc7193643/
edoc-URL https://edoc.unibas.ch/80717/
Full Text on edoc No
Digital Object Identifier DOI 10.1038/s41598-020-64383-9
PubMed ID http://www.ncbi.nlm.nih.gov/pubmed/32355261
 
   

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