Optimized parametric skin modelling for diagnosis of skin abnormalities by combining light back-scatter and laser speckle imaging

Orun, A. B, Goodyer, E., Seker, Huseyin, Smith, G., Uslan, V. and Chauhan, D. (2014) Optimized parametric skin modelling for diagnosis of skin abnormalities by combining light back-scatter and laser speckle imaging. Skin Research and Technology, 20 (4). pp. 473-485. ISSN 0909-752X

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Abstract

Background/purpose
Optical and parametric skin imaging methods which can efficiently identify invisible sub‐skin features or subtle changes in skin layers are very important for accurate optical skin modelling. In this study, a hybrid method is introduced that helps develop a parametric optical skin model by utilizing interdisciplinary techniques including light back‐scatter analysis, laser speckle imaging, image‐texture analysis and Bayesian inference methods. The model aims to detect subtle skin changes and hence very early signs of skin abnormalities/diseases.

Methods
Light back‐scatter and laser speckle image textural analysis are applied onto the normal and abnormal skin regions (lesions) to generate set of attributes/parameters. These are then optimized by Bayesian inference method in order to build an optimized parametric model.

Results
The attributes selected by Bayesian inference method in the optimization stage were used to build an optimized model and then successfully verified. It was clearly proven that Bayesian inference based optimization process yields good results to build an optimized skin model.

Conclusion
The outcome of this study clearly shows the applicability of this hybrid method in the analysis of skin features and is therefore expected to lead development of non‐invasive and low‐cost instrument for early detection of skin changes.

Item Type:	Article
Subjects:	B800 Medical Technology G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	09 Nov 2018 12:31
Last Modified:	11 Oct 2019 18:45
URI:	http://nrl.northumbria.ac.uk/id/eprint/36595

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