scholarly journals Automated processing of thermal imaging to detect COVID-19

2020 ◽  
Author(s):  
Rafael Y. Brzezinski ◽  
Neta Rabin ◽  
Nir Lewis ◽  
Racheli Peled ◽  
Ariel Kerpel ◽  
...  
Keyword(s):  
Image Processing ◽  
Low Income ◽  
Thermal Imaging ◽  
Point Of Care ◽  
Area Under The Curve ◽  
Screening Tools ◽  
Imaging Device ◽  
Thermal Images ◽  
Image Processing Algorithms ◽  
Processing Algorithms

ABSTRACTRapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on image-processing algorithms and machine learning analysis. We captured thermal images of the back of individuals with and without COVID-19 using a portable thermal camera that connects directly to smartphones. Our novel image processing algorithms automatically extracted multiple texture and shape features of the thermal images and achieved an area under the curve (AUC) of 0.85 in detecting COVID-19 with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. We show, for the first time, that a hand-held thermal imaging device can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings, especially in low-income regions with limited imaging resources.

scholarly journals Automated processing of thermal imaging to detect COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rafael Y. Brzezinski ◽  
Neta Rabin ◽  
Nir Lewis ◽  
Racheli Peled ◽  
Ariel Kerpel ◽  
...  
Keyword(s):  
Image Processing ◽  
Low Income ◽  
Thermal Imaging ◽  
Point Of Care ◽  
Area Under The Curve ◽  
Screening Tools ◽  
Imaging Device ◽  
Thermal Images ◽  
Image Processing Algorithms ◽  
Processing Algorithms

AbstractRapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on advanced image processing algorithms. We captured thermal images of the backs of individuals with and without COVID-19 using a portable thermal camera that connects directly to smartphones. Our novel image processing algorithms automatically extracted multiple texture and shape features of the thermal images and achieved an area under the curve (AUC) of 0.85 in COVID-19 detection with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. In summary, we show, for the first time, that a hand-held thermal imaging device can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings, especially in low-income regions with limited imaging resources.

scholarly journals The Use of Features from Fluorescence, Thermography, and NDVI Imaging to Detect Biotic Stress in Lettuce

Plant Disease ◽  
2018 ◽  
Vol 102 (6) ◽  
pp. 1101-1107 ◽  
Author(s):  
Martin Sandmann ◽  
Rita Grosch ◽  
Jan Graefe
Keyword(s):  
Image Processing ◽  
Biotic Stress ◽  
Thermal Imaging ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Maximum Quantum Yield ◽  
Whole Plant ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Horticultural Practice

Fluorescence, normalized difference vegetation index, and thermal imaging are three frequently used nondestructive methods to detect biotic stress in plants. Due, in part, to the inconsistent results reported in the literature and the lack of measurements on the whole-plant scale, we tested the suitability of a wide variety of variables obtained using these three imaging methods to classify young plants into biotically stressed and nonstressed plants. To this end, we applied the model plant–pathogen system lettuce–Rhizoctonia solani. The relevant data from each image and plant (healthy and diseased) was extracted semiautomatically using sophisticated image processing algorithms. This method enabled us to identify the most appropriate variables via discriminant function and logistic regression analysis: photosystem II maximum quantum yield (Fv/Fm) and fluorescence decline ratio can be used to classify variables with an error ≤0.052. Lettuce seedlings with an Fv/Fm ratio > 0.73 were consistently healthy. In some cases, it was possible to detect infection prior to the appearance of symptoms. Possibilities to transfer the method to horticultural practice are discussed.

Otoconia perfect/imperfect crystals

1994 ◽  
Vol 52 ◽  
pp. 42-43
Author(s):  
César D. Fermin ◽  
Dale Martin
Keyword(s):  
Image Processing ◽  
Room Temperature ◽  
Phosphotungstic Acid ◽  
Gallus Domesticus ◽  
Crystal Matrix ◽  
Organic Templates ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Diffraction Patterns

Otoconia of higher vertebrates are interesting biological crystals that display the diffraction patterns of perfect crystals (e.g., calcite for birds and mammal) when intact, but fail to produce a regular crystallographic pattern when fixed. Image processing of the fixed crystal matrix, which resembles the organic templates of teeth and bone, failed to clarify a paradox of biomineralization described by Mann. Recently, we suggested that inner ear otoconia crystals contain growth plates that run in different directions, and that the arrangement of the plates may contribute to the turning angles seen at the hexagonal faces of the crystals.Using image processing algorithms described earlier, and Fourier Transform function (2FFT) of BioScan Optimas®, we evaluated the patterns in the packing of the otoconia fibrils of newly hatched chicks (Gallus domesticus) inner ears. Animals were fixed in situ by perfusion of 1% phosphotungstic acid (PTA) at room temperature through the left ventricle, after intraperitoneal Nembutal (35mg/Kg) deep anesthesia. Negatives were made with a Hitachi H-7100 TEM at 50K-400K magnifications. The negatives were then placed on a light box, where images were filtered and transferred to a 35 mm camera as described.

Elastic Graph-based Semi-supervised Embedding with Adaptive Loss Regression

2020 ◽  
Keyword(s):  
Image Processing ◽  
International Symposium ◽  
Fast Track ◽  
Electronic Imaging ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Image Processing: Algorithms and Systems proceedings.

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