Novel digital image analysis using fractal dimension for assessment of skin radiance.


Journal

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
ISSN: 1600-0846
Titre abrégé: Skin Res Technol
Pays: England
ID NLM: 9504453

Informations de publication

Date de publication:
Jul 2019
Historique:
received: 04 12 2018
accepted: 12 01 2019
pubmed: 17 2 2019
medline: 25 1 2020
entrez: 17 2 2019
Statut: ppublish

Résumé

Despite a strong desire to quantify skin radiance in the field of cosmetics, there does not exist a robust method to characterize it. Classical shine that quantifies the specular reflection from skin has been commonly used as the metric to characterize radiance. However, it does not always correlate with the perceived radiance as there are many other parameters that inform radiance perception including spatial distribution of shine and color homogeneity. In this work, we propose a novel method using fractal analysis to better characterize radiance by considering the spatial heterogeneity of pixel intensities as well as color evenness. A simulated image library (nine images) from very dull to very bright was created using bare face images of 20 panelists. Product images taken post-product usage were ranked along this library by finding the image in the library that most resembles the product image by our algorithm as well as experts. Additionally, classical shine and color measurements were made as benchmarks. Our results confirm a strong correlation (R Fractal dimension calculation offers higher sensitivity and resolution compared with other descriptors such as classical shine or color heterogeneity. In cases where the image rank is dominated by pixel intensities rather than color evenness, the image ranks resulting from calculating the fractal dimension is comparable with use of classical shine as the ranking parameter.

Sections du résumé

BACKGROUND BACKGROUND
Despite a strong desire to quantify skin radiance in the field of cosmetics, there does not exist a robust method to characterize it. Classical shine that quantifies the specular reflection from skin has been commonly used as the metric to characterize radiance. However, it does not always correlate with the perceived radiance as there are many other parameters that inform radiance perception including spatial distribution of shine and color homogeneity.
MATERIALS AND METHODS METHODS
In this work, we propose a novel method using fractal analysis to better characterize radiance by considering the spatial heterogeneity of pixel intensities as well as color evenness. A simulated image library (nine images) from very dull to very bright was created using bare face images of 20 panelists. Product images taken post-product usage were ranked along this library by finding the image in the library that most resembles the product image by our algorithm as well as experts. Additionally, classical shine and color measurements were made as benchmarks.
RESULTS RESULTS
Our results confirm a strong correlation (R
CONCLUSION CONCLUSIONS
Fractal dimension calculation offers higher sensitivity and resolution compared with other descriptors such as classical shine or color heterogeneity. In cases where the image rank is dominated by pixel intensities rather than color evenness, the image ranks resulting from calculating the fractal dimension is comparable with use of classical shine as the ranking parameter.

Identifiants

pubmed: 30770593
doi: 10.1111/srt.12687
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

564-571

Informations de copyright

© 2019 John Wiley & Sons A/S.Published by John Wiley & Sons Ltd.

Auteurs

Morteza Haeri (M)

Product Performance Evaluation, L'Oreal USA, Clark, New Jersey.

Theresa Phamduy (T)

Product Performance Evaluation, L'Oreal USA, Clark, New Jersey.

Nina Cafone (N)

Product Performance Evaluation, L'Oreal USA, Clark, New Jersey.

Kubra Turkileri (K)

Product Performance Evaluation, L'Oreal USA, Clark, New Jersey.

Diane Velkov (D)

Product Performance Evaluation, L'Oreal USA, Clark, New Jersey.

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Classifications MeSH