Elimination of single-beam substitution error in diffuse reflectance measurements using an integrating sphere

Vidovič, Luka; Majaron, Boris

doi:10.1117/1.jbo.19.2.027006

Cited by 29 publications

(17 citation statements)

References 32 publications

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“…The measurement artifact which arises from inevitable change of the light field inside the IS upon the substitution of the white standard with the sample (a.k.a. single-beam substitution error) was removed by performing additional measurements at the reference port of the IS [41].…”

Section: Measurements Of Drs From Human Skin In Vivomentioning

confidence: 99%

Suitability of diffusion approximation for an inverse analysis of diffuse reflectance spectra from human skin in vivo

et al. 2019

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Diffusion approximation (DA) of the radiative transport equation allows derivation of enclosed solutions for diffuse reflectance from multi-layer scattering structures, such as human skin. Although the DA is known to be inadequate near tissue boundaries and light sources, analytical tractability makes such solutions very attractive for use in noninvasive characterization of biological organs based on measured diffuse reflectance spectra (DRS). For the presented three-layer model of human skin, which enables a good match with DRS in visible spectral range measured with an integrating sphere, the DA solutions systematically overshoot numerically simulated DRS (using Monte Carlo approach) by 1-2 percentage points. However, using the former in inverse analysis of the latter can result in much larger artifacts, most notably overestimations of the melanin and blood contents by up to 15%, which must be considered when analyzing experimental DRS. Despite such systematic errors, the described approach allows simple and robust monitoring of physiological changes in human skin, as demonstrated in tests involving temporary obstruction of blood circulation and seasonal variations due to extensive sun exposure.

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Section: Measurements Of Drs From Human Skin In Vivomentioning

confidence: 99%

Suitability of diffusion approximation for an inverse analysis of diffuse reflectance spectra from human skin in vivo

et al. 2019

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“…100 subsequently acquired spectra (at integration time of 15 ms) were averaged to control the signal-to-noise ratio. The so-called single-beam substitution error, which is significant in such small IS, was removed by numerical pre-processing based on prior analysis [11], thus yielding artifact-free DRS values.…”

Section: Experimental Protocolmentioning

confidence: 99%

Predictive model for the quantitative analysis of human skin using photothermal radiometry and diffuse reflectance spectroscopy

Verdel

Tanevski

Džeroski

et al. 2020

Biomed. Opt. Express

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We have recently introduced a novel methodology for the noninvasive analysis of the structure and composition of human skin in vivo. The approach combines pulsed photothermal radiometry (PPTR), involving time-resolved measurements of mid-infrared emission after irradiation with a millisecond light pulse, and diffuse reflectance spectroscopy (DRS) in the visible part of the spectrum. Simultaneous fitting of both data sets with respective predictions from a numerical model of light transport in human skin enables the assessment of the contents of skin chromophores (melanin, oxy-, and deoxy-hemoglobin), as well as scattering properties and thicknesses of the epidermis and dermis. However, the involved iterative optimization of 14 skin model parameters using a numerical forward model (i.e., inverse Monte Carlo-IMC) is computationally very expensive. In order to overcome this drawback, we have constructed a very fast predictive model (PM) based on machine learning. The PM involves random forests, trained on ∼9,000 examples computed using our forward MC model. We show that the performance of such a PM is very satisfying, both in objective testing using cross-validation and in direct comparisons with the IMC procedure. We also present a hybrid approach (HA), which combines the speed of the PM with versatility of the IMC procedure. Compared with the latter, the HA improves both the accuracy and robustness of the inverse analysis, while significantly reducing the computation times.

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“…Lambertian reflection is a common assumption in eliminating substitution errors for an integrating sphere [15,16]. In Stage I, we also employed this assumption in modeling the lighting deviation caused by the imaged samples.…”

Section: Correction Stage I: White-patch Normalizationmentioning

confidence: 99%

“…Typical solutions to this problem include increasing the sphere size, decreasing the sample port area, using multiple standards with different reflectance levels and introducing an additional reference beam. The methods for eliminating substitution error have been investigated in [15,16].…”

Section: Introductionmentioning

confidence: 99%

Lighting Deviation Correction for Integrating-Sphere Multispectral Imaging Systems

Zou

Shen

et al. 2019

Sensors

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In an integrating sphere multispectral imaging system, measurement inconsistency can arise when acquiring the spectral reflectances of samples. This is because the lighting condition can be changed by the measured samples, due to the multiple light reflections inside the integrating sphere. Besides, owing to non-uniform light transmission of the lens and narrow-band filters, the measured reflectance is spatially dependent. To deal with these problems, we propose a correction method that consists of two stages. The first stage employs a white board to correct non-uniformity and a small white patch to correct lighting deviation, both under the assumption of ideal Lambertian reflection. The second stage uses a polynomial regression model to further remove the lighting inconsistency when measuring non-Lambertian samples. The method is evaluated on image data acquired in a real multispectral imaging system. Experimental results illustrate that our method eliminates the measurement inconsistency considerably. This consequently improves the spectral and colorimetric accuracy in color measurement, which is crucial to practical applications.

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Elimination of single-beam substitution error in diffuse reflectance measurements using an integrating sphere

Cited by 29 publications

References 32 publications

Suitability of diffusion approximation for an inverse analysis of diffuse reflectance spectra from human skin in vivo

Suitability of diffusion approximation for an inverse analysis of diffuse reflectance spectra from human skin in vivo

Predictive model for the quantitative analysis of human skin using photothermal radiometry and diffuse reflectance spectroscopy

Lighting Deviation Correction for Integrating-Sphere Multispectral Imaging Systems

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