uantitative characterization of turbidity by radiative transfer based reflectance imaging
Author
Hu, Xin-Hua; Tian, Peng; Chen, Cheng; Jin, Jiahong; Hong, Heng; Lu, Jun Q.
Abstract
A new and noncontact approach of multispectral reflectance imaging has been developed to inversely determine the absorption coefficient of μ a , the scattering coefficient of μs and the anisotropy factor g of a turbid target from one measured reflectance image. The incident beam was profiled with a diffuse reflectance standard for deriving both measured and calculated reflectance images. A GPU implemented Monte Carlo code was developed to determine the parameters with a conjugate gradient descent algorithm and the existence of unique solutions was shown. We noninvasively determined embedded region thickness in heterogeneous targets and estimated in vivo optical parameters of nevi from 4 patients between 500 and 950nm for melanoma diagnosis to demonstrate the potentials of quantitative reflectance imaging.
Date
2018-04-04
Citation:
APA:
Hu, Xin-Hua, & Tian, Peng, & Chen, Cheng, & Jin, Jiahong, & Hong, Heng, & Lu, Jun Q.. (April 2018).
uantitative characterization of turbidity by radiative transfer based reflectance imaging.
,
(),
-
Hua Hu, "Quantitative characterization of turbidity by radiative transfer based reflectance imaging," Biomed. Opt. Express 9, 2081-2094 (2018). Retrieved from
http://hdl.handle.net/10342/7106
MLA:
Hu, Xin-Hua, and Tian, Peng, and Chen, Cheng, and Jin, Jiahong, and Hong, Heng, and Lu, Jun Q..
"uantitative characterization of turbidity by radiative transfer based reflectance imaging". .
. (),
April 2018.
September 26, 2023.
http://hdl.handle.net/10342/7106.
Chicago:
Hu, Xin-Hua and Tian, Peng and Chen, Cheng and Jin, Jiahong and Hong, Heng and Lu, Jun Q.,
"uantitative characterization of turbidity by radiative transfer based reflectance imaging," , no.
(April 2018),
http://hdl.handle.net/10342/7106 (accessed
September 26, 2023).
AMA:
Hu, Xin-Hua, Tian, Peng, Chen, Cheng, Jin, Jiahong, Hong, Heng, Lu, Jun Q..
uantitative characterization of turbidity by radiative transfer based reflectance imaging. .
April 2018;
():
.
http://hdl.handle.net/10342/7106. Accessed
September 26, 2023.
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