Fiber Features: a dedicated software for the automatic characterization of anthropogenic microfibers from environmental samples
DOI:
https://doi.org/10.47193/mafis.4012027010107Keywords:
Marine pollution, automated image analysis, geodesic skeletal distance, morphometric characterization, weatheringAbstract
Microfibers (MFs) are among the most widespread forms of pollution in the environment. Standard procedures for counting and characterizing MFs typically involve particle extraction from the sampling matrix, filtration, and detailed inspection of the membranes to identify, classify, and characterize all detected items. Physical characterization often includes morphometric measurements such as surface area, length, and width, which significantly increases processing time. Recent advances in computer performance and digital imaging have greatly expanded the potential of image analysis as a tool for studying MFs from environmental samples. However, its broader adoption has been hindered by the challenges associated with automating the analysis process. In this study, we present a reliable and user-friendly software tool for the automatic detection, quantification, and measurement of MFs in digital images. The method was specifically developed for analyzing MFs retained on membranes used during filtration procedures, although its applicability may extend to other settings. The approach relies on the acquisition of high-resolution images, from which MFs are automatically segmented and measured in terms of length, width, and additional physical attributes. Results can be exported in multiple formats for further analysis. We evaluated the accuracy and repeatability of the software by comparing its measurements with manual annotations across a dataset of 118 images. The results demonstrated a high level of agreement and robustness, highlighting the software’s potential to streamline and standardize MF analysis in environmental research.
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Copyright (c) 2026 Rosana P. Di Mauro, Rodrigo E. Russo, Nadia M. Alves, Julieta S. Rodríguez, Julieta Rodriguez, Marina V. Diaz, Juan I. Pastore
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