Textural fingerprints: A comprehensive descriptor for biofilm structure development

Abstract

Automated tools to determine biofilm structure are necessary to interpret large time series of biofilm images. Image analysis based on the evaluation of Spatial Gray Level Dependence Matrices (SGLDM) enabled us to monitor biofilm structure development in response to external disturbances (i.e., periodic increases of wall shear stress) at a large scale (i.e., >1 mm). We applied our method to an experiment conducted in an annular reactor over a 10-week period. Six states of biofilm development were differentiated by their unique structure. Previous exposure to rapidly increased shear influenced the resulting biofilm structure after additional shear increases. In addition, on the scale of the biofilm images, the biofilm structure after a shear increase was spatially heterogeneous and resulted in spatially differentiated regrowth after detachment at different locations in the biofilm. SGLDM was developed further as an alternative to approaches based on image binarization as binarization leads to information loss for low-magnification and low-resolution images. During post-processing of image data, structural states of biofilm development were identified by K-means clustering and data display in Principal Component plots. Quantitatively selected representative images were used to illustrate the meaning of the clusters. Post-treatment of image data was essential for managing several thousands of raw biofilm images and therefore improved the usefulness of the image analysis.