Three-dimensional maps of human skin properties on full face with shadows using 3-D hyperspectral imaging

Gevaux, Lou

doi:10.1117/1.jbo.24.6.066002

Cited by 15 publications

(17 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With a known distance from the light sources, a software algorithm is used to calculate the reflection angle of the light beam or the distance at each point in the light pattern to build up the triangulated geometry information of the structures [ 49 ]. The advantage of the active approach is the use of light projection to enhance the accuracy of facial surface mapping [ 50 ]. Moreover, typically, no additional light is needed during image acquisition because the lighting conditions are fully controlled by the systems, restricting the ambient lighting influence [ 7 ].…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, typically, no additional light is needed during image acquisition because the lighting conditions are fully controlled by the systems, restricting the ambient lighting influence [ 7 ]. However, because the technique relies on capturing the light reflection with sensors, the presence of light-reflective or transparent surfaces could be quite problematic for achieving success using this technology [ 50 ].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Accuracy of Portable Face-Scanning Devices for Obtaining Three-Dimensional Face Models: A Systematic Review and Meta-Analysis

Mai

Kim

Choi

et al. 2020

IJERPH

View full text Add to dashboard Cite

The use of three-dimensional face-scanning systems to obtain facial models is of increasing interest, however, systematic assessments of the reliability of portable face-scan devices have not been widely conducted. Therefore, a systematic review and meta-analysis were performed considering the accuracy of facial models obtained by portable face-scanners in comparison with that of those obtained by stationary face-scanning systems. A systematic literature search was conducted in electronic databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for articles published from 1 January 2009 to 18 March 2020. A total of 2806 articles were identified, with 21 articles available for the narrative review and nine studies available for meta-analysis. The meta-analysis revealed that the accuracy of the digital face models generated by the portable scanners was not significantly different from that of the stationary face-scanning systems (standard mean difference (95% confidence interval) = −0.325 mm (−1.186 to 0.536); z = −0.74; p = 0.459). Within the comparison of the portable systems, no statistically significant difference was found concerning the accuracy of the facial models among scanning methods (p = 0.063). Overall, portable face-scan devices can be considered reliable for obtaining facial models. However, caution is needed when applying face-scanners with respect to scanning device settings, control of involuntary facial movements, landmark and facial region identifications, and scanning protocols.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Accuracy of Portable Face-Scanning Devices for Obtaining Three-Dimensional Face Models: A Systematic Review and Meta-Analysis

Mai

Kim

Choi

et al. 2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…A key limitation of these reflectance and absorbance calculations is the assumption of uniform sample illumination. Several methods are used in hardware to ensure that this assumption remains valid, including: uniform illumination instrumentation [28,29]; 3D shape measurement [30]; reference intrinsic / fluorescence imaging [31][32][33][34]; and ratiometric measurements [35][36][37]. Uniform illumination instrumentation can be achieved with specialised devices such as ring illuminators or diffuse domes, however, there are difficulties in applying these for in vivo imaging, such as during endoscopy, due to the need for bulky illuminating units.…”

Section: Plos Onementioning

confidence: 99%

A background correction method to compensate illumination variation in hyperspectral imaging

2020

View full text Add to dashboard Cite

Hyperspectral imaging (HSI) can measure both spatial (morphological) and spectral (biochemical) information from biological tissues. While HSI appears promising for biomedical applications, interpretation of hyperspectral images can be challenging when data is acquired in complex biological environments. Variations in surface topology or optical power distribution at the sample, encountered for example during endoscopy, can lead to errors in post-processing of the HSI data, compromising disease diagnostic capabilities. Here, we propose a background correction method to compensate for such variations, which estimates the optical properties of illumination at the target based on the normalised spectral profile of the light source and the measured HSI intensity values at a fixed wavelength where the absorption characteristics of the sample are relatively low (in this case, 800 nm). We demonstrate the feasibility of the proposed method by imaging blood samples, tissuemimicking phantoms, and ex vivo chicken tissue. Moreover, using synthetic HSI data composed from experimentally measured spectra, we show the proposed method would improve statistical analysis of HSI data. The proposed method could help the implementation of HSI techniques in practical clinical applications, where controlling the illumination pattern and power is difficult. OPEN ACCESSCitation: Yoon J, Grigoroiu A, Bohndiek SE (2020) A background correction method to compensate illumination variation in hyperspectral imaging. PLoS ONE 15(3): e0229502. https://doi.org/ 10.

show abstract

“…It allows high‐resolution measurement of skin spectral reflectance, whose shape, or “spectral signature,” can be related to skin structure and composition using an optical model. In particular, maps showing skin absorption properties such as oxygen rate, blood volume fraction, or melanin concentration can be generated from hyperspectral images using an optical‐based model 3,4 . This analysis method yields quantitative information, which can be helpful for assessing skin properties objectively.…”

Section: Introductionmentioning

confidence: 99%

“…In our previous works, 3‐5 we have used two hyperspectral systems to acquire skin spectral reflectance, SpectraCam ® , and SpectraFace ® (Newtone Technologies, France). These systems have been specially designed for skin measurement and cancapture hyperspectral images at 31 wavelengths in the visible spectrum in around 2 seconds.…”

Section: Introductionmentioning

confidence: 99%

Real‐time skin chromophore estimation from hyperspectral images using a neural network

Gevaux

Gierschendorf²,

Rengot³

et al. 2020

Skin Research and Technology

Self Cite

View full text Add to dashboard Cite

Background Hyperspectral imaging for in vivo human skin study has shown great potential by providing non‐invasive measurement from which information usually invisible to the human eye can be revealed. In particular, maps of skin parameters including oxygen rate, blood volume fraction, and melanin concentration can be estimated from a hyperspectral image by using an optical model and an optimization algorithm. These applications, relying on hyperspectral images acquired with a high‐resolution camera especially dedicated to skin measurement, have yielded promising results. However, the data analysis process is relatively expensive in terms of computation cost, with calculation of full‐face skin property maps requiring up to 5 hours for 3‐megapixels hyperspectral images. Such a computation time prevents punctual previewing and quality assessment of the maps immediately after acquisition. Methods To address this issue, we have implemented a neural network that models the optimization‐based analysis algorithm. This neural network has been trained on a set of hyperspectral images, acquired from 204 patients and their corresponding skin parameter maps, which were calculated by optimization. Results The neural network is able to generate skin parameter maps that are visually very faithful to the reference maps much more quickly than the optimization‐based algorithm, with computation times as short as 2 seconds for a 3‐megapixel image representing a full face and 0.5 seconds for a 1‐megapixel image representing a smaller area of skin. The average deviation calculated on selected areas shows the network's promising generalization ability, even on wide‐field full‐face images. Conclusion Currently, the network is adequate for preview purposes, providing relatively accurate results in a few seconds.

show abstract

Three-dimensional maps of human skin properties on full face with shadows using 3-D hyperspectral imaging

Cited by 15 publications

References 34 publications

Accuracy of Portable Face-Scanning Devices for Obtaining Three-Dimensional Face Models: A Systematic Review and Meta-Analysis

Accuracy of Portable Face-Scanning Devices for Obtaining Three-Dimensional Face Models: A Systematic Review and Meta-Analysis

A background correction method to compensate illumination variation in hyperspectral imaging

Real‐time skin chromophore estimation from hyperspectral images using a neural network

Contact Info

Product

Resources

About