Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep Networks
Since the COVID-19 pandemic, interest in non-contact diagnostic technologies has grown, leading to increased research into remote biosignal monitoring. The respiratory rate, widely used in previous studies, offers limited insight into pulmonary volume. To redress this, we propose a thermal imaging-b...
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MDPI AG
2025-05-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/12/5/542 |
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| author | Do-Kyeong Lee Jae-Sung Shin Jae-Sung Choi Min-Hyung Choi Min Hong |
| author_facet | Do-Kyeong Lee Jae-Sung Shin Jae-Sung Choi Min-Hyung Choi Min Hong |
| author_sort | Do-Kyeong Lee |
| collection | DOAJ |
| description | Since the COVID-19 pandemic, interest in non-contact diagnostic technologies has grown, leading to increased research into remote biosignal monitoring. The respiratory rate, widely used in previous studies, offers limited insight into pulmonary volume. To redress this, we propose a thermal imaging-based framework for respiratory segmentation aimed at estimating non-invasive pulmonary function. The proposed method uses an optical flow magnitude-based thresholding technique to automatically extract exhalation frames and segment them into frame sequences. A TransUNet-based network, combining a Convolutional Neural Network (CNN) encoder–decoder architecture with a Transformer module in the bottleneck, is trained on these sequences. The model’s Accuracy, Precision, Recall, IoU, Dice, and F1-score were 0.9832, 0.9833, 0.9830, 0.9651, 0.9822, and 0.9831, respectively, which results demonstrate high segmentation performance. The method enables the respiratory volume to be estimated by detecting exhalation behavior, suggesting its potential as a non-contact tool to monitor pulmonary function and estimate lung volume. Furthermore, research on thermal imaging-based respiratory volume analysis remains limited. This study expands upon conventional respiratory rate-based approaches to provide a new direction for respiratory analysis using vision-based techniques. |
| format | Article |
| id | doaj-art-220d2ef890c94a3f82cf75fe22f54fb2 |
| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-220d2ef890c94a3f82cf75fe22f54fb22025-08-20T01:56:25ZengMDPI AGBioengineering2306-53542025-05-0112554210.3390/bioengineering12050542Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep NetworksDo-Kyeong Lee0Jae-Sung Shin1Jae-Sung Choi2Min-Hyung Choi3Min Hong4Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of KoreaDepartment of Software Convergence, Soonchunhyang University, Asan 31538, Republic of KoreaDepartment of Internal Medicine, Cheonan Hospital, College of Medicine, Soonchunhyang University, Cheonan 31151, Republic of KoreaDepartment of Computer Science, Saint Louis University, Louis, MO 63103, USADepartment of Computer Software Engineering, Soonchunhyang University, Asan 31538, Republic of KoreaSince the COVID-19 pandemic, interest in non-contact diagnostic technologies has grown, leading to increased research into remote biosignal monitoring. The respiratory rate, widely used in previous studies, offers limited insight into pulmonary volume. To redress this, we propose a thermal imaging-based framework for respiratory segmentation aimed at estimating non-invasive pulmonary function. The proposed method uses an optical flow magnitude-based thresholding technique to automatically extract exhalation frames and segment them into frame sequences. A TransUNet-based network, combining a Convolutional Neural Network (CNN) encoder–decoder architecture with a Transformer module in the bottleneck, is trained on these sequences. The model’s Accuracy, Precision, Recall, IoU, Dice, and F1-score were 0.9832, 0.9833, 0.9830, 0.9651, 0.9822, and 0.9831, respectively, which results demonstrate high segmentation performance. The method enables the respiratory volume to be estimated by detecting exhalation behavior, suggesting its potential as a non-contact tool to monitor pulmonary function and estimate lung volume. Furthermore, research on thermal imaging-based respiratory volume analysis remains limited. This study expands upon conventional respiratory rate-based approaches to provide a new direction for respiratory analysis using vision-based techniques.https://www.mdpi.com/2306-5354/12/5/542thermal imagingnon-contact pulmonary monitoringU-Net-based segmentationpulmonary diagnostics |
| spellingShingle | Do-Kyeong Lee Jae-Sung Shin Jae-Sung Choi Min-Hyung Choi Min Hong Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep Networks Bioengineering thermal imaging non-contact pulmonary monitoring U-Net-based segmentation pulmonary diagnostics |
| title | Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep Networks |
| title_full | Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep Networks |
| title_fullStr | Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep Networks |
| title_full_unstemmed | Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep Networks |
| title_short | Exhale-Focused Thermal Image Segmentation Using Optical Flow-Based Frame Filtering and Transformer-Aided Deep Networks |
| title_sort | exhale focused thermal image segmentation using optical flow based frame filtering and transformer aided deep networks |
| topic | thermal imaging non-contact pulmonary monitoring U-Net-based segmentation pulmonary diagnostics |
| url | https://www.mdpi.com/2306-5354/12/5/542 |
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