Artificial neural network method for determining optical properties from double-integrating-spheres measurements  被引量:4

Artificial neural network method for determining optical properties from double-integrating-spheres measurements

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作  者:李晨曦 赵会娟 王秋殷 徐可欣 

机构地区:[1]State Key Laboratory of Precision Measuring Technology and Instruments,Tianjin University [2]College of Precision Instruments and Optoelectronics Engineering,Tianjin University

出  处:《Chinese Optics Letters》2010年第2期173-176,共4页中国光学快报(英文版)

基  金:supported by the National Natural Science Foundation of China(No.30870657);the Natural Science Foundation of Tianjin(No.09JCZDJC18200);the 111 Project(No.B07014).

摘  要:measurement of the optical properties of biological tissue is very important for optical diagnosis and therapeutics. An artificial neural network (ANN)-based inverse reconstruction method is introduced to determine the optical properties of turbid media, which is based on the reflectance (R) and transmittance (T) of a thin sample measured by a double-integrating-spheres system. The accuracy and robustness of the method has been validated, and the results show that the root mean square errors (RMSEs) of the absorption coefficient μa and scattering coefficient μ′ reconstruction are less than 0.01 cm-1 and 0.02 cm-1, respectively. The algorithm is not only very accurate in the case of a lower albedo (~0.33), but also very robust to the noise of R and T especially for the μ′ reconstruction.measurement of the optical properties of biological tissue is very important for optical diagnosis and therapeutics. An artificial neural network (ANN)-based inverse reconstruction method is introduced to determine the optical properties of turbid media, which is based on the reflectance (R) and transmittance (T) of a thin sample measured by a double-integrating-spheres system. The accuracy and robustness of the method has been validated, and the results show that the root mean square errors (RMSEs) of the absorption coefficient μa and scattering coefficient μ′ reconstruction are less than 0.01 cm-1 and 0.02 cm-1, respectively. The algorithm is not only very accurate in the case of a lower albedo (~0.33), but also very robust to the noise of R and T especially for the μ′ reconstruction.

关 键 词:Repair TURBIDITY 

分 类 号:O439[机械工程—光学工程] TP183[理学—光学]

 

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