Assessment of features in facial hyperpigmentation: Comparison study between VISIA and CSKIN

Zuo, Ying; Li, Anqi; He, Hailun; Wan, Ruoyu; Li, Yu; Li, Li

doi:10.1111/srt.13216

Cited by 5 publications

(7 citation statements)

References 22 publications

(45 reference statements)

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“…Both the specularly reflected light and diffusely reflected light, detected by the camera, contribute to skin image formation. Professional skin diagnostic equipment used for measuring pigment regions [8,20] selectively captures the diffusely reflected light as a cross-polarized image, providing enhanced features of concentrated melanin and hemoglobin areas. To incorporate this into our pigment analysis model, we restricted the input type to cross-polarized images and minimized the influence of specularly reflected light from the image formation process and the output maps.…”

Section: Image Formationmentioning

confidence: 99%

“…These chromophore analysis techniques can be applied to image pixels, expanding the analysis from point measurement to spatial distribution. Whole optical-based image processing approaches are prevalent in commercial equipment and efficient enough for clinical use [8,9], but most analysis tools are restricted to the manufacturer's imaging equipment, limiting versatility. The independent component analysis (ICA), which is one of the representative methods, requires significant pigment concentration variation within the image to distinguish color components [10].…”

Section: Introductionmentioning

confidence: 99%

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Deep learning‐based pigment analysis model trained with optical approach and ground truth assistance

Jung,

Kim,

Lee

et al. 2023

Journal of Biophotonics

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This study introduces an integrated training method combining the optical approach with ground truth for skin pigment analysis. Deep learning is increasingly applied to skin pigment analysis, primarily melanin and hemoglobin. While regression analysis is a widely used training method to predict ground truth‐like outputs, the input image resolution is restricted by computational resources. The optical approach‐based regression method can alleviate this problem, but compromises performance. We propose a strategy to overcome the limitation of image resolution while preserving performance by incorporating ground truth within the optical approach‐based learning structure. The proposed model decomposes skin images into melanin, hemoglobin, and shading maps, reconstructing them by solving the forward problem with reference to the ground truth for pigments. Evaluation against the VISIA system, a professional diagnostic equipment, yields correlation coefficients of 0.978 for melanin and 0.975 for hemoglobin. Furthermore, our model can produce pigment‐modified images for applications like simulating treatment effects.This article is protected by copyright. All rights reserved.

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Section: Image Formationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning‐based pigment analysis model trained with optical approach and ground truth assistance

Jung,

Kim,

Lee

et al. 2023

Journal of Biophotonics

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“…Although trained physicians may be more accurate in predicting a patient's skin color, the human eye's perception and assessment of skin color can be influenced by various factors 2,11 . In a study, dermatologists' mean visual scores for skin color were similar, but internal consistency was not demonstrated using Kendall's coefficient 12,13 . This inconsistency may be due to dermatologists' tendency to overestimate the severity of erythema and the influence of freckles or melasma on erythema identification.…”

Section: Discussionmentioning

confidence: 98%

“…2,11 In a study, dermatologists' mean visual scores for skin color were similar, but internal consistency was not demonstrated using Kendall's coefficient. 12,13 This inconsistency may be due to der-matologists' tendency to overestimate the severity of erythema and the influence of freckles or melasma on erythema identification. It has been noted that colorimetric measurements of melanin may be biased by fluctuations in skin erythema and vice versa due to partial overlap of wavelengths.…”

Section: Discussionmentioning

confidence: 99%

Analysis of facial redness by comparing VISIA and YLGTD

Huang

Qiu³

et al. 2023

Skin Research and Technology

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Background: Erythema, characterized by redness of the skin, is a common symptom in various facial skin conditions. Recent advancements in image processing and analysis techniques have led to the development of methods for analyzing and assessing skin texture. This study aimed to investigate the correlation between the parameters of "You Look Good Today" (YLGTD) and VISIA in the detection and assessment of facial redness. Materials and Methods:Thirty female subjects participated in this experiment, undergoing assessments using both YLGTD and VISIA. The subjects were evaluated for facial redness, and the feature count results within the red zone were measured by VISIA.YLGTD analyzed the number and percentage of red zone pixels. The assessments were conducted between [specific dates] in [location]. Results:The results demonstrated a significant positive correlation between the feature count results within the red zone measured by VISIA and the number of red zone pixels. Similarly, YLGTD exhibited a significant positive correlation with the number and percentage of red zone pixels. Conclusion:In conclusion, our findings suggest a correlation between YLGTD and VISIA in the measurement of facial erythema. YLGTD can serve as a portable device for primary screening assessments, offering a convenient and reliable method to evaluate facial redness. This research contributes to the development of non-invasive techniques for assessing and monitoring facial skin conditions, providing valuable insights for dermatological diagnosis and cosmetic testing.

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“…However, detecting subtle variations in these attributes can be challenging for the naked eye, potentially leading to subjective interpretations. Therefore, clinical devices like Mexameter for point measurement 6 and imaging devices such as VISIA and CSKIN systems for analyzing spatial distribution information are often employed 7 . Several methods have been proposed for skin pigment analysis, including deriving the melanin and erythema indices through a formula composed of RGB channel intensities, 8 estimating skin chromophore concentrations through spectral information measured in diffuse reflectance spectroscopy 9 and hyperspectral imaging, 10 or acquiring different modulated light images in spatial frequency domain imaging 11 …”

Section: Introductionmentioning

confidence: 99%

Generation of skin tone and pigmented region‐modified images using a pigment discrimination model trained with an optical approach

Jung,

Kim,

Lee

et al. 2023

Skin Research and Technology

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BackgroundSkin tone and pigmented regions, associated with melanin and hemoglobin, are critical indicators of skin condition. While most prior research focuses on pigment analysis, the capability to simulate diverse pigmentation conditions could greatly broaden the range of applications. However, current methodologies have limitations in terms of numerical control and versatility.MethodsWe introduce a hybrid technique that integrates optical methods with deep learning to produce skin tone and pigmented region‐modified images with numerical control. The pigment discrimination model produces melanin, hemoglobin, and shading maps from skin images. The outputs are reconstructed into skin images using a forward problem‐solving approach, with model training aimed at minimizing the discrepancy between the reconstructed and input images. By adjusting the melanin and hemoglobin maps, we create pigment‐modified images, allowing precise control over changes in melanin and hemoglobin levels. Changes in pigmentation are quantified using the individual typology angle (ITA) for skin tone and melanin and erythema indices for pigmented regions, validating the intended modifications.ResultsThe pigment discrimination model achieved correlation coefficients with clinical equipment of 0.915 for melanin and 0.931 for hemoglobin. The alterations in the melanin and hemoglobin maps exhibit a proportional correlation with the ITA and pigment indices in both quantitative and qualitative assessments. Additionally, regions overlaying melanin and hemoglobin are demonstrated to verify independent adjustments.ConclusionThe proposed method offers an approach to generate modified images of skin tone and pigmented regions. Potential applications include visualizing alterations for clinical assessments, simulating the effects of skincare products, and generating datasets for deep learning.

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Assessment of features in facial hyperpigmentation: Comparison study between VISIA and CSKIN

Cited by 5 publications

References 22 publications

Deep learning‐based pigment analysis model trained with optical approach and ground truth assistance

Deep learning‐based pigment analysis model trained with optical approach and ground truth assistance

Analysis of facial redness by comparing VISIA and YLGTD

Generation of skin tone and pigmented region‐modified images using a pigment discrimination model trained with an optical approach

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