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Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting
Cephalometric analysis is a standard tool for assessment and prediction of craniofacial growth, orthodontic diagnosis, and oral-maxillofacial treatment planning. The aim of this study is to develop a fully automatic system of cephalometric analysis, including cephalometric landmark detection and cep...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6276415/ https://www.ncbi.nlm.nih.gov/pubmed/30581546 http://dx.doi.org/10.1155/2018/1797502 |
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author | Wang, Shumeng Li, Huiqi Li, Jiazhi Zhang, Yanjun Zou, Bingshuang |
author_facet | Wang, Shumeng Li, Huiqi Li, Jiazhi Zhang, Yanjun Zou, Bingshuang |
author_sort | Wang, Shumeng |
collection | PubMed |
description | Cephalometric analysis is a standard tool for assessment and prediction of craniofacial growth, orthodontic diagnosis, and oral-maxillofacial treatment planning. The aim of this study is to develop a fully automatic system of cephalometric analysis, including cephalometric landmark detection and cephalometric measurement in lateral cephalograms for malformation classification and assessment of dental growth and soft tissue profile. First, a novel method of multiscale decision tree regression voting using SIFT-based patch features is proposed for automatic landmark detection in lateral cephalometric radiographs. Then, some clinical measurements are calculated by using the detected landmark positions. Finally, two databases are tested in this study: one is the benchmark database of 300 lateral cephalograms from 2015 ISBI Challenge, and the other is our own database of 165 lateral cephalograms. Experimental results show that the performance of our proposed method is satisfactory for landmark detection and measurement analysis in lateral cephalograms. |
format | Online Article Text |
id | pubmed-6276415 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-62764152018-12-23 Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting Wang, Shumeng Li, Huiqi Li, Jiazhi Zhang, Yanjun Zou, Bingshuang J Healthc Eng Research Article Cephalometric analysis is a standard tool for assessment and prediction of craniofacial growth, orthodontic diagnosis, and oral-maxillofacial treatment planning. The aim of this study is to develop a fully automatic system of cephalometric analysis, including cephalometric landmark detection and cephalometric measurement in lateral cephalograms for malformation classification and assessment of dental growth and soft tissue profile. First, a novel method of multiscale decision tree regression voting using SIFT-based patch features is proposed for automatic landmark detection in lateral cephalometric radiographs. Then, some clinical measurements are calculated by using the detected landmark positions. Finally, two databases are tested in this study: one is the benchmark database of 300 lateral cephalograms from 2015 ISBI Challenge, and the other is our own database of 165 lateral cephalograms. Experimental results show that the performance of our proposed method is satisfactory for landmark detection and measurement analysis in lateral cephalograms. Hindawi 2018-11-19 /pmc/articles/PMC6276415/ /pubmed/30581546 http://dx.doi.org/10.1155/2018/1797502 Text en Copyright © 2018 Shumeng Wang et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Wang, Shumeng Li, Huiqi Li, Jiazhi Zhang, Yanjun Zou, Bingshuang Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting |
title | Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting |
title_full | Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting |
title_fullStr | Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting |
title_full_unstemmed | Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting |
title_short | Automatic Analysis of Lateral Cephalograms Based on Multiresolution Decision Tree Regression Voting |
title_sort | automatic analysis of lateral cephalograms based on multiresolution decision tree regression voting |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6276415/ https://www.ncbi.nlm.nih.gov/pubmed/30581546 http://dx.doi.org/10.1155/2018/1797502 |
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