Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates

Yoon, Seung-Chul; Shin, Tae-Sung; Park, Bosoon; Lawrence, Kurt C.; Heitschmidt, Gerald W.

doi:10.1117/12.2041085

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…This approach of spectral reconstruction from RGB data could potentially offer simple instrumentation and operation, using the currently available smartphone technologies. 21,35,36 In this respect, we performed numerical experiments to test the feasibility that a blood Hgb level can be sensitively and accurately predicted using a three-color sensor-based system in a similar manner of our previous study. 21 First, RGB signals were acquired from measured reflectance spectra data.…”

Section: Full Spectral Reconstruction From Rgbmentioning

confidence: 99%

“…21,35,36 As the first step for realizing blood Hgb testcomparable photometric anemia detection superior to the conventional color chart methods (e.g., FAMACHA), we investigated spectral reconstruction from RGB data that are commonly acquired from conventional cameras. This approach of spectral reconstruction from RGB data could potentially offer simple instrumentation and operation, using the currently available smartphone technologies.…”

Section: Full Spectral Reconstruction From Rgbmentioning

confidence: 99%

“…Then, we can predict a reflectance spectrumr from a new RGB signal x by multiplying x by T. To improve the prediction accuracy for spectral reconstruction, we selected multivariate second-order polynomial regression with leave-one-out crossvalidation. 21,35 Third, using the reconstructed reflectance spectra, we reliably quantified blood Hgb levels using the PLSR method described in Sec. 2.1.…”

Section: Full Spectral Reconstruction From Rgbmentioning

confidence: 99%

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Toward laboratory blood test-comparable photometric assessments for anemia in veterinary hematology

et al. 2016

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Abstract. Anemia associated with intestinal parasites and malnutrition is the leading cause of morbidity and mortality in small ruminants worldwide. Qualitative scoring of conjunctival redness has been developed so that farmers can gauge anemia in sheep and goats to identify animals that require treatment. For clinically relevant anemia diagnosis, complete blood count-comparable quantitative methods often rely on complicated and expensive optical instruments, requiring detailed spectral information of hemoglobin. We report experimental and numerical results for simple, yet reliable, noninvasive hemoglobin detection that can be correlated with laboratory-based blood hemoglobin testing for anemia diagnosis. In our pilot animal study using calves, we exploit the third eyelid (i.e., palpebral conjunctiva) as an effective sensing site. To further test spectrometerfree (or spectrometerless) hemoglobin assessments, we implement full spectral reconstruction from RGB data and partial least square regression. The unique combination of RGB-based spectral reconstruction and partial least square regression could potentially offer uncomplicated instrumentation and avoid the use of a spectrometer, which is vital for realizing a compact and inexpensive hematology device for quantitative anemia detection in the farm field.

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Section: Full Spectral Reconstruction From Rgbmentioning

confidence: 99%

Section: Full Spectral Reconstruction From Rgbmentioning

confidence: 99%

Section: Full Spectral Reconstruction From Rgbmentioning

confidence: 99%

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Toward laboratory blood test-comparable photometric assessments for anemia in veterinary hematology

et al. 2016

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“…Fortunately, several methods and techniques for reconstructing full spectra from RGB data have been studied extensively, such as Wiener estimation [8][9][10][20][21][22] and regression methods. [23][24][25] Suppose that a reflectance spectrum r and a corresponding RGB spectrum x are collected for each sample. Then,R m×N and X m×3 can be formed by stacking the spectra from several independent samples, where m is the number of different samples and N is the number of wavelengths.…”

Section: Reconstruction Of Hyperspectral Image Data From Rgb Datamentioning

confidence: 99%

“…In particular, the prediction ability for full spectral construction can be improved with polynomial regression. 23,24 In our case, a possibly nonlinear relationship between full and RGB spectra can be captured by extending…”

Section: Reconstruction Of Hyperspectral Image Data From Rgb Datamentioning

confidence: 99%

Nonspectroscopic imaging for quantitative chlorophyll sensing

Kim

Visbal-Onufrak

et al. 2016

J. Biomed. Opt

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Nondestructive imaging of physiological changes in plants has been intensively used as an invaluable tool for visualizing heterogeneous responses to various types of abiotic and biotic stress. However, conventional approaches often have intrinsic limitations for quantitative analyses, requiring bulky and expensive optical instruments for capturing full spectral information. We report a spectrometerless (or spectrometer-free) reflectance imaging method that allows for nondestructive and quantitative chlorophyll imaging in individual leaves in situ in a handheld device format. The combination of a handheld-type imaging system and a hyperspectral reconstruction algorithm from an RGB camera offers simple instrumentation and operation while avoiding the use of an imaging spectrograph or tunable color filter. This platform could potentially be integrated into a compact, inexpensive, and portable system, while being of great value in high-throughput phenotyping facilities and laboratory settings.

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Spectral Reconstruction for Paired Images Based on Semi-Supervised Deep Learning

Li,

Liu,

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Hyperspectral image reconstruction using RGB color for foodborne pathogen detection on agar plates

Cited by 3 publications

References 11 publications

Toward laboratory blood test-comparable photometric assessments for anemia in veterinary hematology

Toward laboratory blood test-comparable photometric assessments for anemia in veterinary hematology

Nonspectroscopic imaging for quantitative chlorophyll sensing

Spectral Reconstruction for Paired Images Based on Semi-Supervised Deep Learning

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