Graph-based deep learning segmentation of EDS spectral images for automated mineral phase analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU147761" target="_blank" >RIV/00216305:26230/22:PU147761 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0098300422000668" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0098300422000668</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cageo.2022.105109" target="_blank" >10.1016/j.cageo.2022.105109</a>

Result language

angličtina

Original language name

Graph-based deep learning segmentation of EDS spectral images for automated mineral phase analysis

Original language description

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.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

COMPUTERS & GEOSCIENCES

ISSN

0098-3004

e-ISSN

1873-7803

Volume of the periodical

165

Issue of the periodical within the volume

8

Country of publishing house

GB - UNITED KINGDOM

Number of pages

2

Pages from-to

1-2

UT code for WoS article

000817165900005

EID of the result in the Scopus database

2-s2.0-85131055092