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22 April 2016 Comprehensive Monte-Carlo simulator for optimization of imaging parameters for high sensitivity detection of skin cancer at the THz
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Abstract
Skin cancer detection at its early stages has been the focus of a large number of experimental and theoretical studies during the past decades. Among these studies two prominent approaches presenting high potential are reflectometric sensing at the THz wavelengths region and polarimetric imaging techniques in the visible wavelengths. While THz radiation contrast agent and source of sensitivity to cancer related tissue alterations was considered to be mainly the elevated water content in the cancerous tissue, the polarimetric approach has been verified to enable cancerous tissue differentiation based on cancer induced structural alterations to the tissue. Combining THz with the polarimetric approach, which is considered in this study, is examined in order to enable higher detection sensitivity than previously pure reflectometric THz measurements. For this, a comprehensive MC simulation of radiative transfer in a complex skin tissue model fitted for the THz domain that considers the skin`s stratified structure, tissue material optical dispersion modeling, surface roughness, scatterers, and substructure organelles has been developed. Additionally, a narrow beam Mueller matrix differential analysis technique is suggested for assessing skin cancer induced changes in the polarimetric image, enabling the tissue model and MC simulation to be utilized for determining the imaging parameters resulting in maximal detection sensitivity.
Conference Presentation
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael Ney and Ibrahim Abdulhalim "Comprehensive Monte-Carlo simulator for optimization of imaging parameters for high sensitivity detection of skin cancer at the THz", Proc. SPIE 9721, Nanoscale Imaging, Sensing, and Actuation for Biomedical Applications XIII, 97210W (22 April 2016); https://doi.org/10.1117/12.2209486
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