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Computer Vision-Based Image Analysis of Bacteria

Danielsen, Jonas and Nordenfelt, Pontus LU (2017) In Bacterial Pathogenesis 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.

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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Bacterial Pathogenesis
editor
Nordenfelt, Pontus; Collin, Matthias; and
volume
1535
pages
12 pages
publisher
Springer New York
external identifiers
  • 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
2018-01-07 11:50:48
@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},
  editor       = {Nordenfelt, Pontus and Collin, Matthias},
  isbn         = {978-1-4939-6671-4},
  issn         = {1064-3745},
  language     = {eng},
  pages        = {161--172},
  publisher    = {Springer New York},
  series       = {Bacterial Pathogenesis},
  title        = {Computer Vision-Based Image Analysis of Bacteria},
  url          = {http://dx.doi.org/10.1007/978-1-4939-6673-8_10},
  volume       = {1535},
  year         = {2017},
}