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Infrared Sensing and Thermal Imaging for Biomedical Engineering
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Infrared Sensing and Thermal Imaging for Biomedical Engineering

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 30 April 2024 | Viewed by 5271

Special Issue Editors

Dr. David Perpetuini

E-Mail Website
Guest Editor
Department of Engineering and Geology, University of G. d'Annunzio Chieti and Pescara, 65127 Pescara, Italy
Interests: infrared thermography; functional infrared spectroscopy (fNIRS); electroencephalography (EEG); photoplethysmography (PPG); wearable sensors; affective computing; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Damiano Formenti

E-Mail Website
Guest Editor
Department of Biotechnology and Life Sciences (DBSV), University of Insubria, Via Dunant, 3, 21100 Varese, Italy
Interests: infrared thermography; physical activity; sport sciences; cognition and exercise; health behavior
Dr. Daniela Cardone

E-Mail Website
Guest Editor
Department of Engineering and Geology, University of G. d'Annunzio Chieti and Pescara, 65127 Pescara, Italy
Interests: artificial intelligence methods; robotics and affective computing; human–machine interaction; processing methods and analysis of biomedical images and physiological signals; computer vision
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nicola Ludwig

E-Mail Website
Guest Editor
Department of Physics, State University of Milano, via Celoria 16, 20133 Milano, Italy
Interests: thermography; multispectral imaging; heat transfer; segmentation algorithm; materials characterization; NDT

Special Issue Information

Dear Colleagues,

Infrared sensing is a non-invasive technique employed in several biomedical applications, from cardiovascular monitoring to neuroimaging. Particularly, infrared sensing is widely employed in clinical practice, affective computing, telemedicine applications, and sport science, as well as for research purposes. The diffusion of this methodology is fostered by the improvements of infrared detector technology, the growth in computational power, and the development of innovative algorithms for data analysis, such as novel approaches based on artificial intelligence. Moreover, infrared sensing is particularly suitable for multimodal integration, providing a complete investigation of the human psychophysiological condition.

The aim of this Special Issue is to promote the exchange of experiences, information and methods among developers of infrared sensors and researchers that use such technology for biomedical engineering applications. This synergistic cooperation will extend the infrared sensing application fields, favoring technological improvements and encouraging the development of innovative algorithms for data analysis, software, and procedures. Original papers that describe new research or innovative biomedical applications, as well as invited reviews on the topics, are welcome. We look forward to your participation in this Special Issue.

Dr. David Perpetuini
Dr. Damiano Formenti
Dr. Daniela Cardone
Prof. Dr. Nicola Ludwig
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • infrared thermography
  • functional near-infrared spectroscopy
  • photoplethysmography
  • machine learning
  • infrared sensors
  • segmentation algorithm

Published Papers (4 papers)

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Research

14 pages, 3016 KiB  
Article
Thermal Imaging of the Tongue Surface as a Predictive Method in the Diagnosis of Type 2 Diabetes Mellitus
by Daria Wziątek-Kuczmik, Antoni Świątkowski, Armand Cholewka, Aleksandra Mrowiec, Iwona Niedzielska and Agata Stanek
Sensors 2024, 24(8), 2447; https://doi.org/10.3390/s24082447 - 11 Apr 2024
Viewed by 277
Abstract
Over the past 20 years, the high prevalence of diabetes has become a global public health problem. Background: The objective of this study was to develop a non-invasive screening method for diabetes which will enable the detection of the disease at an early [...] Read more.
Over the past 20 years, the high prevalence of diabetes has become a global public health problem. Background: The objective of this study was to develop a non-invasive screening method for diabetes which will enable the detection of the disease at an early stage. Methods: This study included 63 adult patients of both sexes: 30 patients with type 2 diabetes (t2DM) and 33 healthy volunteers. The temperature distribution on the tongue’s dorsum and apex surface was studied in patients after a mouth-cooling procedure had been introduced. The study used an FLIR T540 thermal imaging camera. An analysis of the correlation between the ∆T values of the tongue dorsum and apex and the glycated hemoglobin (HbA1c) level was performed. Results: The median of the average dorsum temperature measured 10 min after mouth rinsing was almost 0.8 [°C] lower than for healthy individuals. Also, studies showed a positive average correlation with a Pearson coefficient of r = 0.46 between the HbA1c level and the ∆T of the tongue dorsum. Conclusions: Tongue temperature measured using the IRT showed a correlation with standard biochemical parameters; it may also differentiate patients and constitute a specific screening method for patients with t2DM. Full article
(This article belongs to the Special Issue Infrared Sensing and Thermal Imaging for Biomedical Engineering)
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15 pages, 6692 KiB  
Article
Automatic Segmentation of Facial Regions of Interest and Stress Detection Using Machine Learning
by Daniel Jaramillo-Quintanar, Jean K. Gomez-Reyes, Luis A. Morales-Hernandez, Benjamin Dominguez-Trejo, David A. Rodriguez-Medina and Irving A. Cruz-Albarran
Sensors 2024, 24(1), 152; https://doi.org/10.3390/s24010152 - 27 Dec 2023
Viewed by 831
Abstract
Stress is a factor that affects many people today and is responsible for many of the causes of poor quality of life. For this reason, it is necessary to be able to determine whether a person is stressed or not. Therefore, it is [...] Read more.
Stress is a factor that affects many people today and is responsible for many of the causes of poor quality of life. For this reason, it is necessary to be able to determine whether a person is stressed or not. Therefore, it is necessary to develop tools that are non-invasive, innocuous, and easy to use. This paper describes a methodology for classifying stress in humans by automatically detecting facial regions of interest in thermal images using machine learning during a short Trier Social Stress Test. Five regions of interest, namely the nose, right cheek, left cheek, forehead, and chin, are automatically detected. The temperature of each of these regions is then extracted and used as input to a classifier, specifically a Support Vector Machine, which outputs three states: baseline, stressed, and relaxed. The proposal was developed and tested on thermal images of 25 participants who were subjected to a stress-inducing protocol followed by relaxation techniques. After testing the developed methodology, an accuracy of 95.4% and an error rate of 4.5% were obtained. The methodology proposed in this study allows the automatic classification of a person’s stress state based on a thermal image of the face. This represents an innovative tool applicable to specialists. Furthermore, due to its robustness, it is also suitable for online applications. Full article
(This article belongs to the Special Issue Infrared Sensing and Thermal Imaging for Biomedical Engineering)
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17 pages, 3253 KiB  
Article
Diabetic Plantar Foot Segmentation in Active Thermography Using a Two-Stage Adaptive Gamma Transform and a Deep Neural Network
by Zhenjie Cao, Zhi Zeng, Jinfang Xie, Hao Zhai, Ying Yin, Yue Ma and Yibin Tian
Sensors 2023, 23(20), 8511; https://doi.org/10.3390/s23208511 - 17 Oct 2023
Viewed by 980
Abstract
Pathological conditions in diabetic feet cause surface temperature variations, which can be captured quantitatively using infrared thermography. Thermal images captured during recovery of diabetic feet after active cooling may reveal richer information than those from passive thermography, but diseased foot regions may exhibit [...] Read more.
Pathological conditions in diabetic feet cause surface temperature variations, which can be captured quantitatively using infrared thermography. Thermal images captured during recovery of diabetic feet after active cooling may reveal richer information than those from passive thermography, but diseased foot regions may exhibit very small temperature differences compared with the surrounding area, complicating plantar foot segmentation in such cold-stressed active thermography. In this study, we investigate new plantar foot segmentation methods for thermal images obtained via cold-stressed active thermography without the complementary information from color or depth channels. To better deal with the temporal variations in thermal image contrast when planar feet are recovering from cold immersion, we propose an image pre-processing method using a two-stage adaptive gamma transform to alleviate the impact of such contrast variations. To improve upon existing deep neural networks for segmenting planar feet from cold-stressed infrared thermograms, a new deep neural network, the Plantar Foot Segmentation Network (PFSNet), is proposed to better extract foot contours. It combines the fundamental U-shaped network structure, a multi-scale feature extraction module, and a convolutional block attention module with a feature fusion network. The PFSNet, in combination with the two-stage adaptive gamma transform, outperforms multiple existing deep neural networks in plantar foot segmentation for single-channel infrared images from cold-stressed infrared thermography, achieving an accuracy of 97.3% and 95.4% as measured by Intersection over Union (IOU) and Dice Similarity Coefficient (DSC) respectively. Full article
(This article belongs to the Special Issue Infrared Sensing and Thermal Imaging for Biomedical Engineering)
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14 pages, 3365 KiB  
Article
Dynamic Vascular Imaging Using Active Breast Thermography
by Meir Gershenson and Jonathan Gershenson
Sensors 2023, 23(6), 3012; https://doi.org/10.3390/s23063012 - 10 Mar 2023
Cited by 3 | Viewed by 2579
Abstract
Mammography is considered the gold standard for breast cancer screening and diagnostic imaging; however, there is an unmet clinical need for complementary methods to detect lesions not characterized by mammography. Far-infrared ‘thermogram’ breast imaging can map the skin temperature, and signal inversion with [...] Read more.
Mammography is considered the gold standard for breast cancer screening and diagnostic imaging; however, there is an unmet clinical need for complementary methods to detect lesions not characterized by mammography. Far-infrared ‘thermogram’ breast imaging can map the skin temperature, and signal inversion with components analysis can be used to identify the mechanisms of thermal image generation of the vasculature using dynamic thermal data. This work focuses on using dynamic infrared breast imaging to identify the thermal response of the stationary vascular system and the physiologic vascular response to a temperature stimulus affected by vasomodulation. The recorded data are analyzed by converting the diffusive heat propagation into a virtual wave and identifying the reflection using component analysis. Clear images of passive thermal reflection and thermal response to vasomodulation were obtained. In our limited data, the magnitude of vasoconstriction appears to depend on the presence of cancer. The authors propose future studies with supporting diagnostic and clinical data that may provide validation of the proposed paradigm. Full article
(This article belongs to the Special Issue Infrared Sensing and Thermal Imaging for Biomedical Engineering)
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