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.
Key words
- Image segmentation
- Object recognition
- Region properties
- MATLAB
- ImageJ
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Acknowledgments
This work was supported by the Crafoord Foundation, the Swedish Research Council, the Swedish Society of Medicine, and the O.E. and Edla Johansson Foundation.
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Danielsen, J., Nordenfelt, P. (2017). Computer Vision-Based Image Analysis of Bacteria. In: Nordenfelt, P., Collin, M. (eds) Bacterial Pathogenesis. Methods in Molecular Biology, vol 1535. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6673-8_10
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DOI: https://doi.org/10.1007/978-1-4939-6673-8_10
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