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Open AccessFeature PaperArticle

Incremental Low Rank Noise Reduction for Robust Infrared Tracking of Body Temperature during Medical Imaging

1
Computer Vision and System Laboratory (CVSL), Department of Electrical and Computer Engineering, Laval University, Quebec City, QC G1V 0A6, Canada
2
RT Thermal Co., 7167 Elkhorn Drive, West Palm Beach, FL 33411, USA
3
Visiooimage Inc., 2560, Rue Lapointe, Sainte-Foy, Quebec City, QC G1W 1A8, Canada
*
Author to whom correspondence should be addressed.
Current address: Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
Electronics 2019, 8(11), 1301; https://doi.org/10.3390/electronics8111301
Received: 21 September 2019 / Revised: 1 November 2019 / Accepted: 4 November 2019 / Published: 7 November 2019
(This article belongs to the Special Issue Design and Application of Biomedical Circuits and Systems)
Thermal imagery for monitoring of body temperature provides a powerful tool to decrease health risks (e.g., burning) for patients during medical imaging (e.g., magnetic resonance imaging). The presented approach discusses an experiment to simulate radiology conditions with infrared imaging along with an automatic thermal monitoring/tracking system. The thermal tracking system uses an incremental low-rank noise reduction applying incremental singular value decomposition (SVD) and applies color based clustering for initialization of the region of interest (ROI) boundary. Then a particle filter tracks the ROI(s) from the entire thermal stream (video sequence). The thermal database contains 15 subjects in two positions (i.e., sitting, and lying) in front of thermal camera. This dataset is created to verify the robustness of our method with respect to motion-artifacts and in presence of additive noise (2–20%—salt and pepper noise). The proposed approach was tested for the infrared images in the dataset and was able to successfully measure and track the ROI continuously (100% detecting and tracking the temperature of participants), and provided considerable robustness against noise (unchanged accuracy even in 20% additive noise), which shows promising performance. View Full-Text
Keywords: infrared and thermal image analysis; incremental low rank noise reduction; incremental singular value decomposition; segmentation; monitoring of body temperature; particle filter tracking infrared and thermal image analysis; incremental low rank noise reduction; incremental singular value decomposition; segmentation; monitoring of body temperature; particle filter tracking
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Yousefi, B.; Memarzadeh Sharifipour, H.; Eskandari, M.; Ibarra-Castanedo, C.; Laurendeau, D.; Watts, R.; Klein, M.; Maldague, X.P.V. Incremental Low Rank Noise Reduction for Robust Infrared Tracking of Body Temperature during Medical Imaging. Electronics 2019, 8, 1301.

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