Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Quantitative Optical Imaging Using Random Walk Theory

Not Accessible

Your library or personal account may give you access

Abstract

The robustness of a random walk model that uses time-dependent contrast functions to quantify the cross-section and the corrected scattering and absorption coefficients of optically abnormal targets from time-of-flight (TOF) data obtained in time-resolved transillumination experiments is successfully analyzed. Several independent sets of experimental TOF data are used to show that the random walk methodology is able to quantify the size and optical properties of embedded targets with an error ≤25%. The underlying theoretical assumptions of the model are tested. Finally, the effect of lateral boundaries on time-resolved measurements of light transmitted through slabs of finite thickness is considered within the framework of a random walk model.

© 2000 Optical Society of America

PDF Article
More Like This
Features and performance of a tomographic algorithm, based on a random walk model, for quantification of the optical characteristics of an abnormality embedded within tissue-like turbid media.

Victor Chernomordik, David Hattery, Amir H. Gandjbakhche, Antonio Pifferi, Paola Taroni, Alessandro Torricelli, Gianluca Valentini, Rinaldo Cubeddu, and Jeremy C. Hebden
AMB2 Biomedical Topical Meeting (BIOMED) 1999

A new algorithm based on time-dependent contrast functions, used to evaluate optical characteristics of an abnormality hidden within a tissue-like phantom

Victor Chemomordik, Jeremy C. Hebden, Ralph Nossal, and Amir H. Gandjbakhche
ATuD34 Advances in Optical Imaging and Photon Migration (BIOMED) 1998

Optical detection of abnormally absorbing regions in tissue

Amir H. Gandjbakhche, Victor Chernomordik, Robert F. Bonner, Ralph Nossal, George H. Weiss, and Jeremy Hebden
MT183 Advances in Optical Imaging and Photon Migration (BIOMED) 1996

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.