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Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening †

Fever screening based on infrared (IR) thermographs (IRTs) is an approach that has been implemented during infectious disease pandemics, such as Ebola and Severe Acute Respiratory Syndrome. A recently published international standard indicates that regions medially adjacent to the inner canthi provi...

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Autores principales: Dwith Chenna, Yedukondala Narendra, Ghassemi, Pejhman, Pfefer, T. Joshua, Casamento, Jon, Wang, Quanzeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795541/
https://www.ncbi.nlm.nih.gov/pubmed/29300320
http://dx.doi.org/10.3390/s18010125
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author Dwith Chenna, Yedukondala Narendra
Ghassemi, Pejhman
Pfefer, T. Joshua
Casamento, Jon
Wang, Quanzeng
author_facet Dwith Chenna, Yedukondala Narendra
Ghassemi, Pejhman
Pfefer, T. Joshua
Casamento, Jon
Wang, Quanzeng
author_sort Dwith Chenna, Yedukondala Narendra
collection PubMed
description Fever screening based on infrared (IR) thermographs (IRTs) is an approach that has been implemented during infectious disease pandemics, such as Ebola and Severe Acute Respiratory Syndrome. A recently published international standard indicates that regions medially adjacent to the inner canthi provide accurate estimates of core body temperature and are preferred sites for fever screening. Therefore, rapid, automated identification of the canthi regions within facial IR images may greatly facilitate rapid fever screening of asymptomatic travelers. However, it is more difficult to accurately identify the canthi regions from IR images than from visible images that are rich with exploitable features. In this study, we developed and evaluated techniques for multi-modality image registration (MMIR) of simultaneously captured visible and IR facial images for fever screening. We used free form deformation (FFD) models based on edge maps to improve registration accuracy after an affine transformation. Two widely used FFD models in medical image registration based on the Demons and cubic B-spline algorithms were qualitatively compared. The results showed that the Demons algorithm outperformed the cubic B-spline algorithm, likely due to overfitting of outliers by the latter method. The quantitative measure of registration accuracy, obtained through selected control point correspondence, was within 2.8 ± 1.2 mm, which enables accurate and automatic localization of canthi regions in the IR images for temperature measurement.
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spelling pubmed-57955412018-02-13 Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening † Dwith Chenna, Yedukondala Narendra Ghassemi, Pejhman Pfefer, T. Joshua Casamento, Jon Wang, Quanzeng Sensors (Basel) Article Fever screening based on infrared (IR) thermographs (IRTs) is an approach that has been implemented during infectious disease pandemics, such as Ebola and Severe Acute Respiratory Syndrome. A recently published international standard indicates that regions medially adjacent to the inner canthi provide accurate estimates of core body temperature and are preferred sites for fever screening. Therefore, rapid, automated identification of the canthi regions within facial IR images may greatly facilitate rapid fever screening of asymptomatic travelers. However, it is more difficult to accurately identify the canthi regions from IR images than from visible images that are rich with exploitable features. In this study, we developed and evaluated techniques for multi-modality image registration (MMIR) of simultaneously captured visible and IR facial images for fever screening. We used free form deformation (FFD) models based on edge maps to improve registration accuracy after an affine transformation. Two widely used FFD models in medical image registration based on the Demons and cubic B-spline algorithms were qualitatively compared. The results showed that the Demons algorithm outperformed the cubic B-spline algorithm, likely due to overfitting of outliers by the latter method. The quantitative measure of registration accuracy, obtained through selected control point correspondence, was within 2.8 ± 1.2 mm, which enables accurate and automatic localization of canthi regions in the IR images for temperature measurement. MDPI 2018-01-04 /pmc/articles/PMC5795541/ /pubmed/29300320 http://dx.doi.org/10.3390/s18010125 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dwith Chenna, Yedukondala Narendra
Ghassemi, Pejhman
Pfefer, T. Joshua
Casamento, Jon
Wang, Quanzeng
Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening †
title Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening †
title_full Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening †
title_fullStr Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening †
title_full_unstemmed Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening †
title_short Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening †
title_sort free-form deformation approach for registration of visible and infrared facial images in fever screening †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5795541/
https://www.ncbi.nlm.nih.gov/pubmed/29300320
http://dx.doi.org/10.3390/s18010125
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