Mining Artifacts in Mycelium SEM Micrographs
This work addresses a domain-specific issue for researchers studying mycelium biomaterials, offering an incremental improvement over existing tools.
The paper tackled the problem of imaging artifacts in mycelium SEM micrographs, which hinder accurate characterization of its nanofibrous structure, by combining supervised and unsupervised machine learning methods to automate artifact identification.
Mycelium is a promising biomaterial based on fungal mycelium, a highly porous, nanofibrous structure. Scanning electron micrographs are used to characterize its network, but the currently available tools for nanofibrous microstructures do not contemplate the particularities of biomaterials. The adoption of a software for artificial nanofibrous in mycelium characterization adds the uncertainty of imaging artifact formation to the analysis. The reported work combines supervised and unsupervised machine learning methods to automate the identification of artifacts in the mapped pores of mycelium microstructure. Keywords: Machine learning; unsupervised learning; image processing; mycelium; microstructure informatics