Melanin type and concentration determination using inverse model

Abnormality of melanin production causes skin pigmentation disorders. Currently, assessment of treatment efficacy (under Physician's Global Assessment framework) only refers to visual conditions of skin surface and not the condition of the underlying skin layers and pigments. Albeit researches on models and simulations of light interaction with human skin have been reported, none has been specifically developed for pigmentation analysis of melanin types - eumelanin and pheomelanin. Therefore, our research objectives are to develop image analysis of skin pigmentation for classification and quantification of eumelanin and pheomelanin pigment types in human skin. In this research, the model is developed using data collected from clinical study. It is hypothesised that the multispectral approach will provide an accurate characterisation of skin layers to determine melanin types namely eumelanin and pheomelanin. Monte Carlo method is then used to determine skin model parameters such as concentration of eumelanin and pheomelanin.

[1]  L Wang,et al.  MCML--Monte Carlo modeling of light transport in multi-layered tissues. , 1995, Computer methods and programs in biomedicine.

[2]  Ela Claridge,et al.  Developing a predictive model of human skin coloring , 1996, Medical Imaging.

[3]  Motonori Doi,et al.  Spectral estimation of human skin color using the Kubelka-Munk theory , 2003, IS&T/SPIE Electronic Imaging.

[4]  M Carrara,et al.  Optical devices used for image analysis of pigmented skin lesions: a proposal for quality assurance protocol using tissue-like phantoms , 2006, Physics in medicine and biology.

[5]  G. Monfrecola,et al.  Phaeomelanin versus eumelanin as a chemical indicator of ultraviolet sensitivity in fair-skinned subjects at high risk for melanoma: a pilot study , 1998, Melanoma research.

[6]  S. A. Prahl,et al.  A Monte Carlo model of light propagation in tissue , 1989, Other Conferences.

[7]  R. Anderson,et al.  ANALYTICAL MODELING FOR THE OPTICAL PROPERTIES OF THE SKIN WITH IN VITRO AND IN VIVO APPLICATIONS , 1981, Photochemistry and photobiology.

[8]  R. Anderson,et al.  The optics of human skin. , 1981, The Journal of investigative dermatology.

[9]  Norimichi Tsumura,et al.  Mapping Pigmentation in Human Skin by Multi-Visible-Spectral Imaging by Inverse Optical Scattering Technique , 2000, Color Imaging Conference.

[10]  H. Nugroho,et al.  Independent component analysis for assessing therapeutic response in vitiligo skin disorder , 2009, Journal of medical engineering & technology.

[11]  Norimichi Tsumura,et al.  Independent Component Analysis of Skin Color Image , 1998, CIC.

[12]  N. Metropolis,et al.  The Monte Carlo method. , 1949 .

[13]  E. Claridge,et al.  Spectrophotometric intracutaneous analysis: a new technique for imaging pigmented skin lesions , 2002, The British journal of dermatology.

[14]  Norimichi Tsumura,et al.  Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin , 2003, ACM Trans. Graph..

[15]  G. Malash,et al.  Piecewise linear regression: A statistical method for the analysis of experimental adsorption data by the intraparticle-diffusion models , 2010 .

[16]  M Itoh,et al.  Melanin and blood concentration in a human skin model studied by multiple regression analysis: assessment by Monte Carlo simulation , 2001, Physics in medicine and biology.

[17]  K. Wakamatsu,et al.  Eumelanin and phaeomelanin contents of depigmented and repigmented skin in vitiligo patients , 2003, The British journal of dermatology.

[18]  H.J.C.M. Sterenborg,et al.  Skin optics , 1989, IEEE Transactions on Biomedical Engineering.

[19]  Symon Cotton,et al.  A noninvasive skin imaging system , 1997 .

[20]  Gladimir V. G. Baranoski,et al.  A Biophysically‐Based Spectral Model of Light Interaction with Human Skin , 2004, Comput. Graph. Forum.