Assessment of unsupervised clustering of label-free x-ray fluorescence microscopy data

M. A. Z. Chowdhury; K. Ok; Y. Luo; Z. Liu; S. Chen; T. V. O'Halloran; R. Kettimuthu; A. Tekawade

doi:10.1117/12.2632862

3 October 2022 Assessment of unsupervised clustering of label-free x-ray fluorescence microscopy data

M. A. Z. Chowdhury, K. Ok, Y. Luo, Z. Liu, S. Chen, T. V. O'Halloran, R. Kettimuthu, A. Tekawade

Proceedings Volume 12227, Applications of Machine Learning 2022; 122270N (2022) https://doi.org/10.1117/12.2632862
Event: SPIE Optical Engineering + Applications, 2022, San Diego, California, United States

Abstract

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.

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M. A. Z. Chowdhury, K. Ok, Y. Luo, Z. Liu, S. Chen, T. V. O'Halloran, R. Kettimuthu, and A. Tekawade "Assessment of unsupervised clustering of label-free x-ray fluorescence microscopy data", Proc. SPIE 12227, Applications of Machine Learning 2022, 122270N (3 October 2022); https://doi.org/10.1117/12.2632862

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