Texture-based characterization of subskin features by specified laser speckle effects at λ = 650 nm region for more accurate parametric 'skin age' modelling.

OBJECTIVE: The textural structure of 'skin age'-related subskin components enables us to identify and analyse their unique characteristics, thus making substantial progress towards establishing an accurate skin age model. METHODS: This is achieved by a two-stage process. First by the application of textural analysis using laser speckle imaging, which is sensitive to textural effects within the λ = 650 nm spectral band region. In the second stage, a Bayesian inference method is used to select attributes from which a predictive model is built. RESULTS: This technique enables us to contrast different skin age models, such as the laser speckle effect against the more widely used normal light (LED) imaging method, whereby it is shown that our laser speckle-based technique yields better results. CONCLUSION: The method introduced here is non-invasive, low cost and capable of operating in real time; having the potential to compete against high-cost instrumentation such as confocal microscopy or similar imaging devices used for skin age identification purposes.

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

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.

Viscoelastic measurements after vocal fold scarring in rabbits--short-term results after hyaluronan injection.

CONCLUSIONS: The scarring model resulted in significant damage and elevated viscoelasticity of the lamina propria. Hyaluronan preparations may alter viscoelasticity in scarred rabbit vocal folds. OBJECTIVES: Vocal fold scarring results in stiffness of the lamina propria and severe voice problems. The aims of this study were to examine the degree of scarring achieved in the experiment and to measure the viscoelastic properties after injection of hyaluronan in rabbit vocal folds. MATERIALS AND METHODS: Twenty-two vocal folds from 15 New Zealand rabbits were scarred, 8 vocal folds were controls. After 8 weeks 12 of the scarred vocal folds received injections with 2 types of cross-linked hyaluronan products and 10 scarred folds were injected with saline. After 11 more weeks the animals were sacrificed. After dissection, 15 vocal folds were frozen for viscoelastic measurements, whereas 14 vocal folds were prepared and stained. Measurements were made of the lamina propria thickness. Viscoelasticity was measured on intact vocal folds with a linear skin rheometer (LSR) adapted to laryngeal measurements. RESULTS: Measurements on the digitized slides showed a thickened lamina propria in the scarred samples as compared with the normal vocal folds (p<0.05). The viscoelastic analysis showed a tendency to stiffening of the scarred vocal folds as compared with the normal controls (p=0.05). There was large variation in stiffness between the two injected hyaluronan products.

Prediction of skin ages by means of multi-spectral light sources.

Assessment of skin aging is a complex biological process that depends on various internal and external factors but has become important due to personalized skin health and cosmetic treatments. Although there are a small number of attempts to assess the skin aging, they identify only one of the previously classified skin aging groups. The methods used to achieve it are also based generally on highly expensive measurement devices. This work therefore proposes novel low-cost skin aging assessment apparatus by using light back-scatter intensity level of Red, Blue, Green and Infrared bands. This is further enhanced by using a machine learning method to accurately predict actual skin age. The proposed method appears to be highly capable of capturing multi-layer cellular changes exhibited by the biological skin aging process and predicting skin ages with a root-mean-square error of as low as 0.160 by using only four features based on the four multi-spectral light sources. This assessment kit seems to be the first of its kind, which is expected to bring great benefit to both personalized skin healthcare and cosmetic sectors.