Computational characterization of reflectance confocal microscopy features reveals potential for automated photoageing assessment

Raphael, Anthony P., Kelf, Timothy A., Wurm, Elizabeth M. T., Zvyagin, Andrei V., Soyer, Hans Peter and Prow, Tarl W. (2013) Computational characterization of reflectance confocal microscopy features reveals potential for automated photoageing assessment. Experimental Dermatology, 22 7: 458-463. doi:10.1111/exd.12176


Author Raphael, Anthony P.
Kelf, Timothy A.
Wurm, Elizabeth M. T.
Zvyagin, Andrei V.
Soyer, Hans Peter
Prow, Tarl W.
Title Computational characterization of reflectance confocal microscopy features reveals potential for automated photoageing assessment
Journal name Experimental Dermatology   Check publisher's open access policy
ISSN 0906-6705
1600-0625
Publication date 2013-07
Sub-type Article (original research)
DOI 10.1111/exd.12176
Open Access Status
Volume 22
Issue 7
Start page 458
End page 463
Total pages 6
Place of publication Chichester, West Sussex United Kingdom
Publisher Wiley-Blackwell Publishing
Collection year 2014
Language eng
Formatted abstract
Skin photoageing results from a combination of factors including ultraviolet (sun) exposure, leading to significant changes in skin morphology and composition. Conventional methods assessing the degree of photoageing, in particular histopathological assessment involve an invasive multistep process. Advances in microscopy have enabled a shift towards non-invasive in vivo microscopy techniques such as reflectance confocal microscopy (RCM) in this context. Computational image analysis of RCM images has the potential to be of use in the non-invasive assessment of photoageing. In this report, we computationally characterized a clinical RCM data set from younger and older Caucasians with varying levels of photoageing. We identified several mathematical relationships that related to the degree of photoageing as assessed by conventional scoring approaches (clinical photography, SCINEXA and RCM). Furthermore, by combining the mathematical features into a single computational assessment score, we observed significant correlations with conventional RCM (P < 0.0001) and the other clinical assessment techniques.
Keyword Computational analysis
Non-melanoma skin cancer
Photoageing
Reflectance confocal microscopy
Skin
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Medicine Publications
 
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