Mining Artifacts in Mycelium SEM Micrographs

TL;DR

This paper introduces machine learning techniques to automate artifact detection in SEM micrographs of mycelium, improving the analysis of its nanofibrous network for biomaterial characterization.

Contribution

It presents a novel combination of supervised and unsupervised machine learning methods tailored for artifact identification in mycelium microstructure images.

Findings

Effective artifact detection in SEM images of mycelium

Enhanced microstructure analysis accuracy

Potential for improved biomaterial characterization

Abstract

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