Biofilm Image Segmentation Using Optimal Multi-level Thresholding

A microbial biofilm is structured mainly by a protective sticky matrix of extracellular polymeric substances. Quantifying such structures is useful for microbiologists and a correct image segmentation process helps substantially reduce errors in quantification. This paper proposes an approach to segmentation of biofilm images using optimal multilevel thresholding and indices of clustering validity. A direct comparison through Rand index and a quantification process is performed in a laboratory, obtaining results similar to the quantification and segmentation done by an expert.