ArticleJournal of biomedical optics2018
Machine learning approach for rapid and accurate estimation of optical properties using spatial frequency domain imaging.
Article in Journal of biomedical optics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Simulation-Driven Spatial Frequency Domain Imaging and Deep Learning for Subsurface Fruit Bruise Discrimination.Foods (Basel, Switzerland) · 2026Article
- Structured light imaging mesoscopy: detection of embedded morphological changes in superficial tissues.Journal of biomedical optics · 2025Article
- Deep-learning-enabled spatial frequency domain imaging of the spatiotemporal dynamics of skin physiology.Journal of biomedical optics · 2025Article
- Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue.Journal of biomedical optics · 2024Article
- Reconstruction of Optical Properties in Turbid Media: Omitting the Need of the Collimated Transmission for an Integrating Sphere Setup.Sensors (Basel, Switzerland) · 2024Article
- Tutorial on methods for estimation of optical absorption and scattering properties of tissue.Journal of biomedical optics · 2024Article
- Deep-learning approach to stratified reconstructions of tissue absorption and scattering in time-domain spatial frequency domain imaging.Journal of biomedical optics · 2024Article
- Designing a use-error robust machine learning model for quantitative analysis of diffuse reflectance spectra.Journal of biomedical optics · 2024Article
- Neural network-based inverse model for diffuse reflectance spectroscopy.Biomedical optics express · 2023Article
- Physics-guided neural network for tissue optical properties estimation.Biomedical optics express · 2023Article
- OpticalBERT and OpticalTable-SQA: Text- and Table-Based Language Models for the Optical-Materials Domain.Journal of chemical information and modeling · 2023Article
- Model for the diffuse reflectance in spatial frequency domain imaging.Journal of biomedical optics · 2023Article
- Quantification of bowel ischaemia using real-time multispectral Single Snapshot Imaging of Optical Properties (SSOP).Surgical endoscopy · 2023Article
- Extracting Tissue Optical Properties and Detecting Bruised Tissue in Pears Quickly and Accurately Based on Spatial Frequency Domain Imaging and Machine Learning.Foods (Basel, Switzerland) · 2023Article
- Training generative adversarial networks for optical property mapping using synthetic image data.Biomedical optics express · 2022Article
- A database of refractive indices and dielectric constants auto-generated using ChemDataExtractor.Scientific data · 2022Article
- Toward the development of a polarimetric tool to diagnose the fibrotic human ventricular myocardium.Journal of biomedical optics · 2022Article
- Multispectral snapshot imaging of skin microcirculatory hemoglobin oxygen saturation using artificial neural networks trained on in vivo data.Journal of biomedical optics · 2022Article
- Deep learning in macroscopic diffuse optical imaging.Journal of biomedical optics · 2022Review
- Spatial frequency domain imaging technology based on Fourier single-pixel imaging.Journal of biomedical optics · 2022Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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