Publication Details
Graph-based deep learning segmentation of EDS spectral images for automated mineral phase analysis
VÝRAVSKÝ, J.
Kolář Martin, M.Sc., Ph.D. et Ph.D.
Motl David, Ing.
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)
Segmentation, Deep learning, EDS spectra, Automated mineralogy
We introduce a novel method for graph-based segmentation of spectral images
obtained using a Scanning Electron Microscope (SEM) equipped with an Energy
Dispersive X-ray spectroscopy (EDS) detector. The method exploits deep learning
along with fusion of rasterized electron microscopy images with sparse EDS
samples to obtain accurate mineralogy segmentation with high efficiency.
Improvements over previous methods are with respect to the goal of an improved
quantitative and qualitative assessment of segmentation, so that volumetric
composition is indirectly addressed. We describe the principles of the novel
method, show experimental results on real samples and demonstrate its advantages
in comparison to the state of the art. The new method performs unsupervised
clustering on sparsely measured EDS spectra, allowing for classification of
unseen mineralogical compounds. Then, the processed spectra are combined with
single channel SEM measurements through an optimized lattice, where a Markov
Field is used to perform spatial segmentation in image. The benefit of this
material-agnostic method is that clusters can then be (separately) classified,
analyzed, and small grains with distinct EDS measurements are more accurately
separated than in previous methods. These improved results are evaluated
quantitatively on ground-truth electron microscope measurements with dense
high-count EDS data, as well as visually through analysis by a mineralogist.
@article{BUT182942,
author="JURÁNEK, R. and VÝRAVSKÝ, J. and KOLÁŘ, M. and MOTL, D. and ZEMČÍK, P.",
title="Graph-based deep learning segmentation of EDS spectral images for automated mineral phase analysis",
journal="COMPUTERS & GEOSCIENCES",
year="2022",
volume="165",
number="8",
pages="1--2",
doi="10.1016/j.cageo.2022.105109",
issn="0098-3004",
url="https://www.sciencedirect.com/science/article/pii/S0098300422000668"
}