We previously demonstrated a machine learning based regions-of-interest (ROI) finding tool for X-ray fluorescence microscopy, called XRF-ROI-Finder at the 9-ID beamline in Argonne National Laboratory.1 Bacterial cell treatment type prediction and recommendation for steering experiments were performed via the application of fuzzy k-means clustering algorithm. ROI-Finder takes the fluorescence microscopy images, performs segmentation and detects individual E.coli cells, extracts features for principal component analysis, and ultimately performs label-free clustering for cell treatment type prediction and recommendation for similar cells to perform automatic steering experimentation. In this paper, we assess two additional clustering method, namely hierarchical agglomerative clustering (HAC) and density based spatial clustering of applications with noise (DBSCAN) algorithm. The ROI-Finder software is hosted at https://github.com/aisteer/ROI-Finder.
Applications of X-ray computed tomography (CT) for porosity characterization of engineering materials often
involve an extended data analysis workflow that includes CT reconstruction of raw projection data, binarization, labeling and mesh extraction. It is often desirable to map the porosity in larger samples but the computational challenge of reducing gigabytes of raw data to porosity information poses a critical bottleneck. In this work, we describe algorithms and implementation of an end-to-end porosity mapping code "Tomo2Mesh" that processes raw projection data from a synchrotron CT instrument into a porosity measurement and visualization within minutes on a single high-performance workstation equipped with GPU.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.