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An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images

Several efforts have been made to completely automate cephalometric analysis by automatic landmark search. However, accuracy obtained was worse than manual identification in every study. The analogue-to-digital conversion of X-ray has been claimed to be the main problem. Therefore the aim of this in...

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Autores principales: Leonardi, Rosalia, Giordano, Daniela, Maiorana, Francesco
Formato: Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2742650/
https://www.ncbi.nlm.nih.gov/pubmed/19753320
http://dx.doi.org/10.1155/2009/717102
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author Leonardi, Rosalia
Giordano, Daniela
Maiorana, Francesco
author_facet Leonardi, Rosalia
Giordano, Daniela
Maiorana, Francesco
author_sort Leonardi, Rosalia
collection PubMed
description Several efforts have been made to completely automate cephalometric analysis by automatic landmark search. However, accuracy obtained was worse than manual identification in every study. The analogue-to-digital conversion of X-ray has been claimed to be the main problem. Therefore the aim of this investigation was to evaluate the accuracy of the Cellular Neural Networks approach for automatic location of cephalometric landmarks on softcopy of direct digital cephalometric X-rays. Forty-one, direct-digital lateral cephalometric radiographs were obtained by a Siemens Orthophos DS Ceph and were used in this study and 10 landmarks (N, A Point, Ba, Po, Pt, B Point, Pg, PM, UIE, LIE) were the object of automatic landmark identification. The mean errors and standard deviations from the best estimate of cephalometric points were calculated for each landmark. Differences in the mean errors of automatic and manual landmarking were compared with a 1-way analysis of variance. The analyses indicated that the differences were very small, and they were found at most within 0.59 mm. Furthermore, only few of these differences were statistically significant, but differences were so small to be in most instances clinically meaningless. Therefore the use of X-ray files with respect to scanned X-ray improved landmark accuracy of automatic detection. Investigations on softcopy of digital cephalometric X-rays, to search more landmarks in order to enable a complete automatic cephalometric analysis, are strongly encouraged.
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spelling pubmed-27426502009-09-14 An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images Leonardi, Rosalia Giordano, Daniela Maiorana, Francesco J Biomed Biotechnol Research Article Several efforts have been made to completely automate cephalometric analysis by automatic landmark search. However, accuracy obtained was worse than manual identification in every study. The analogue-to-digital conversion of X-ray has been claimed to be the main problem. Therefore the aim of this investigation was to evaluate the accuracy of the Cellular Neural Networks approach for automatic location of cephalometric landmarks on softcopy of direct digital cephalometric X-rays. Forty-one, direct-digital lateral cephalometric radiographs were obtained by a Siemens Orthophos DS Ceph and were used in this study and 10 landmarks (N, A Point, Ba, Po, Pt, B Point, Pg, PM, UIE, LIE) were the object of automatic landmark identification. The mean errors and standard deviations from the best estimate of cephalometric points were calculated for each landmark. Differences in the mean errors of automatic and manual landmarking were compared with a 1-way analysis of variance. The analyses indicated that the differences were very small, and they were found at most within 0.59 mm. Furthermore, only few of these differences were statistically significant, but differences were so small to be in most instances clinically meaningless. Therefore the use of X-ray files with respect to scanned X-ray improved landmark accuracy of automatic detection. Investigations on softcopy of digital cephalometric X-rays, to search more landmarks in order to enable a complete automatic cephalometric analysis, are strongly encouraged. Hindawi Publishing Corporation 2009 2009-09-10 /pmc/articles/PMC2742650/ /pubmed/19753320 http://dx.doi.org/10.1155/2009/717102 Text en Copyright © 2009 Rosalia Leonardi et al. 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
Leonardi, Rosalia
Giordano, Daniela
Maiorana, Francesco
An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images
title An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images
title_full An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images
title_fullStr An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images
title_full_unstemmed An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images
title_short An Evaluation of Cellular Neural Networks for the Automatic Identification of Cephalometric Landmarks on Digital Images
title_sort evaluation of cellular neural networks for the automatic identification of cephalometric landmarks on digital images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2742650/
https://www.ncbi.nlm.nih.gov/pubmed/19753320
http://dx.doi.org/10.1155/2009/717102
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