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Sensors 2018, 18(1), 125; doi:10.3390/s18010125

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

1
Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
2
Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20740, USA
This paper is an extended version of our proceedings article: Dwith, C.Y.N.; Ghassemi, P.; Pfefer, J.; Casamento, J.;Wang, Q. Multi-modality image registration for effective thermographic fever screening. In Proceedings of the SPIE 10057, Multimodal Biomedical Imaging XII, 100570S, San Francisco, CA, USA, 15 February 2017.
*
Author to whom correspondence should be addressed.
Received: 17 November 2017 / Revised: 18 December 2017 / Accepted: 1 January 2018 / Published: 4 January 2018
(This article belongs to the Special Issue Noncontact and Unobtrusive Biomedical Sensors 2017)
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Abstract

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. View Full-Text
Keywords: thermal imaging; fever screening; temperature measurement; canthi detection; multi-modality image registration; free form deformation; Demons algorithm; cubic B-spline algorithm thermal imaging; fever screening; temperature measurement; canthi detection; multi-modality image registration; free form deformation; Demons algorithm; cubic B-spline algorithm
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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. (CC BY 4.0).

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Dwith Chenna, Y.N.; Ghassemi, P.; Pfefer, T.J.; Casamento, J.; Wang, Q. Free-Form Deformation Approach for Registration of Visible and Infrared Facial Images in Fever Screening. Sensors 2018, 18, 125.

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