Semi-Automatic Segmentation of Pseudomonas koreensis and Escherichia coli for Bacterial Growth Characterization

Bacterial characterization is a crucial discipline within microbiology. Given the manual and labor-intensive nature of this task, our aim is to introduce a semi-automatic segmentation method that enhances efficiency while preserving the rich details of bacterial colonies. We propose using the k-means clusterization algorithm to analyze and segment images of bacterial cultures, specifically those of Pseudomonas koreensis and Escherichia coli. Unlike existing methods that focus primarily on colony counting, our approach emphasizes morphological characterization. In some bacterial cultures, colonies are not well-defined, making manual counting or other automated counting methods unfeasible; i.e. the bacterial growth area is not easily identifiable, thus precise growth tracking is not feasible. Our method enables bacterial growth characterization even in these cases. Our computer vision system identifies and quantifies the diverse morphologies within P. koreensis and E. coli cultures, determining their relative occupancy in an image. Our approach provides valuable insights into the composition, growth patterns, and developmental stages of bacterial colonies, designed to assist both novice and expert microbiologists in bacterial analysis.