Abstract:
:Fast estimation of optical properties from reflectance measurements at two spatial frequencies could pave way for real-time, wide-field and quantitative mapping of vital signs of tissues. We present a machine learning-based approach for estimating optical properties in the spatial frequency domain, where a random forest regression algorithm is trained over data obtained from Monte-Carlo photon transport simulations. The algorithm learns the nonlinear mapping between diffuse reflectance at two spatial frequencies, and the absorption and reduced scattering coefficient of the tissue under consideration. Using this method, absorption and reduced scattering properties could be obtained over a 1 megapixel image in 450 ms with errors as low as 0.556% in absorption and 0.126% in reduced scattering.
journal_name
J Biomed Optjournal_title
Journal of biomedical opticsauthors
Panigrahi S,Gioux Sdoi
10.1117/1.JBO.24.7.071606subject
Has Abstractpub_date
2018-12-01 00:00:00pages
1-6issue
7eissn
1083-3668issn
1560-2281pii
JBO-180505SSLRjournal_volume
24pub_type
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