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Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm

Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repa...

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Autores principales: Naebi, Mohammad, Saberi, Eshaghali, Risbaf Fakour, Sirous, Naebi, Ahmad, Hosseini Tabatabaei, Somayeh, Ansari Moghadam, Somayeh, Bozorgmehr, Elham, Davtalab Behnam, Nasim, Azimi, Hamidreza
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860259/
https://www.ncbi.nlm.nih.gov/pubmed/27212947
http://dx.doi.org/10.1155/2016/3264545
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author Naebi, Mohammad
Saberi, Eshaghali
Risbaf Fakour, Sirous
Naebi, Ahmad
Hosseini Tabatabaei, Somayeh
Ansari Moghadam, Somayeh
Bozorgmehr, Elham
Davtalab Behnam, Nasim
Azimi, Hamidreza
author_facet Naebi, Mohammad
Saberi, Eshaghali
Risbaf Fakour, Sirous
Naebi, Ahmad
Hosseini Tabatabaei, Somayeh
Ansari Moghadam, Somayeh
Bozorgmehr, Elham
Davtalab Behnam, Nasim
Azimi, Hamidreza
author_sort Naebi, Mohammad
collection PubMed
description Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO) and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%.
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spelling pubmed-48602592016-05-22 Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm Naebi, Mohammad Saberi, Eshaghali Risbaf Fakour, Sirous Naebi, Ahmad Hosseini Tabatabaei, Somayeh Ansari Moghadam, Somayeh Bozorgmehr, Elham Davtalab Behnam, Nasim Azimi, Hamidreza Int J Dent Research Article Background/Purpose. In terms of the detection of tooth diagnosis, no intelligent detection has been done up till now. Dentists just look at images and then they can detect the diagnosis position in tooth based on their experiences. Using new technologies, scientists will implement detection and repair of tooth diagnosis intelligently. In this paper, we have introduced one intelligent method for detection using particle swarm optimization (PSO) and our mathematical formulation. This method was applied to 2D special images. Using developing of our method, we can detect tooth diagnosis for all of 2D and 3D images. Materials and Methods. In recent years, it is possible to implement intelligent processing of images by high efficiency optimization algorithms in many applications especially for detection of dental caries and restoration without human intervention. In the present work, we explain PSO algorithm with our detection formula for detection of dental caries and restoration. Also image processing helped us to implement our method. And to do so, pictures taken by digital radiography systems of tooth are used. Results and Conclusion. We implement some mathematics formula for fitness of PSO. Our results show that this method can detect dental caries and restoration in digital radiography pictures with the good convergence. In fact, the error rate of this method was 8%, so that it can be implemented for detection of dental caries and restoration. Using some parameters, it is possible that the error rate can be even reduced below 0.5%. Hindawi Publishing Corporation 2016 2016-04-26 /pmc/articles/PMC4860259/ /pubmed/27212947 http://dx.doi.org/10.1155/2016/3264545 Text en Copyright © 2016 Mohammad Naebi et al. https://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
Naebi, Mohammad
Saberi, Eshaghali
Risbaf Fakour, Sirous
Naebi, Ahmad
Hosseini Tabatabaei, Somayeh
Ansari Moghadam, Somayeh
Bozorgmehr, Elham
Davtalab Behnam, Nasim
Azimi, Hamidreza
Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm
title Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm
title_full Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm
title_fullStr Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm
title_full_unstemmed Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm
title_short Detection of Carious Lesions and Restorations Using Particle Swarm Optimization Algorithm
title_sort detection of carious lesions and restorations using particle swarm optimization algorithm
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4860259/
https://www.ncbi.nlm.nih.gov/pubmed/27212947
http://dx.doi.org/10.1155/2016/3264545
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