Computer Vision-Based Image Analysis of Bacteria
Danielsen, Jonas and Nordenfelt, Pontus LU
(2017)
In Methods in Molecular Biology
1535.
p.161-172
- 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... (More)
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.
(Less)
- author
- Danielsen, Jonas
and Nordenfelt, Pontus
LU
- organization
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Bacterial Pathogenesis : Methods and Protocols - Methods and Protocols
- series title
- Methods in Molecular Biology
- editor
- Nordenfelt, Pontus and Collin, Matthias
- volume
- 1535
- pages
- 12 pages
- publisher
- Springer
- external identifiers
-
- pmid:27914078
- scopus:85005949211
- ISSN
- 1064-3745
- ISBN
- 978-1-4939-6671-4
- 978-1-4939-6673-8
- DOI
- 10.1007/978-1-4939-6673-8_10
- language
- English
- LU publication?
- yes
- id
- 708ea7d3-506b-4246-be08-4913d1677b62
- date added to LUP
- 2017-02-16 08:01:23
- date last changed
- 2025-01-07 07:27:11
@inbook{708ea7d3-506b-4246-be08-4913d1677b62,
abstract = {{<p>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.</p>}},
author = {{Danielsen, Jonas and Nordenfelt, Pontus}},
booktitle = {{Bacterial Pathogenesis : Methods and Protocols}},
editor = {{Nordenfelt, Pontus and Collin, Matthias}},
isbn = {{978-1-4939-6671-4}},
issn = {{1064-3745}},
language = {{eng}},
pages = {{161--172}},
publisher = {{Springer}},
series = {{Methods in Molecular Biology}},
title = {{Computer Vision-Based Image Analysis of Bacteria}},
url = {{http://dx.doi.org/10.1007/978-1-4939-6673-8_10}},
doi = {{10.1007/978-1-4939-6673-8_10}},
volume = {{1535}},
year = {{2017}},
}