title: Computer Vision-Based Image Analysis of Bacteria.
abstract: Microscopy is an essential tool for studying bacteria, but is today mostly used in a qualitative or possibly semi-quantitative manner often involving time-consuming manual analysis. It also makes it difficult to assess the importance of individual bacterial phenotypes, especially when there are only subtle differences in features such as shape, size, or signal intensity, which is typically very difficult for the human eye to discern. With computer vision-based image analysis - where computer algorithms interpret image data - it is possible to achieve an objective and reproducible quantification of images in an automated fashion. Besides being a much more efficient and consistent way to analyze images, this can also reveal important information that was previously hard to extract with traditional methods. Here, we present basic concepts of automated image processing, segmentation and analysis that can be relatively easy implemented for use with bacterial research.
authors.affiliation: Division of Infection Medicine, Department of Clinical Sciences, Lund University, BMC B14, 221 84, Lund, Sweden.
authors.forename: Jonas
authors.initials: J
authors.lastname: Danielsen
authors.affiliation: Division of Infection Medicine, Department of Clinical Sciences, Lund University, BMC B14, 221 84, Lund, Sweden. pontus.nordenfelt@med.lu.se.
authors.forename: Pontus
authors.initials: P
authors.lastname: Nordenfelt
mesh_terms: D001419:Bacteria; D019295:Computational Biology / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008853:Microscopy / Q000379:methods; D057054:Molecular Imaging / Q000379:methods*; D012984:Software
keywords: Image segmentation; ImageJ; MATLAB; Object recognition; Region properties