Mineral segmentation using electron microscope images and spectral sampling through multimodal graph neural networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0197861" target="_blank" >RIV/00216305:26230/26:0197861 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.patrec.2025.04.012" target="_blank" >https://doi.org/10.1016/j.patrec.2025.04.012</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.patrec.2025.04.012" target="_blank" >10.1016/j.patrec.2025.04.012</a>

Result language
angličtina
Original language name
Mineral segmentation using electron microscope images and spectral sampling through multimodal graph neural networks
Original language description
We propose a novel Graph Neural Network-based method for segmentation based on data fusion of multimodal Scanning Electron Microscope (SEM) images. In most cases, Backscattered Electron (BSE) images obtained using SEM do not contain sufficient information for mineral segmentation. Therefore, imaging is often complemented with point-wise Energy-Dispersive X-ray Spectroscopy (EDS) spectral measurements that provide highly accurate information about the chemical composition but that are time-consuming to acquire. This motivates the use of sparse spectral data in conjunction with BSE images for mineral segmentation. The unstructured nature of the spectral data makes most traditional image fusion techniques unsuitable for BSE-EDS fusion. We propose using graph neural networks to fuse the two modalities and segment the mineral phases simultaneously. Our results demonstrate that providing EDS data for as few as 1% of BSE pixels produces accurate segmentation, enabling rapid analysis of mineral samples. The proposed data fusion pipeline is versatile and can be adapted to other domains that involve image data and point-wise measurements.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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