ROI-Finder: machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy

ROI-Finder: machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy

The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role...

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Bibliographic Details
Main Authors: M. A. Z. Chowdhury, K. Ok, Y. Luo, Z. Liu, S. Chen, T. V. O'Halloran, R. Kettimuthu, A. Tekawade
Format: Article
Language:English
Published: International Union of Crystallography 2022-11-01
Series:Journal of Synchrotron Radiation
Subjects:
x-ray fluorescence
region-of-interest detection
principal components analysis
live cell imaging
microscopy
e. coli
fuzzy clustering
Online Access:http://scripts.iucr.org/cgi-bin/paper?S1600577522008876

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http://scripts.iucr.org/cgi-bin/paper?S1600577522008876

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