| Chapter title |
Computer Vision-Based Image Analysis of Bacteria.
|
|---|---|
| Chapter number | 10 |
| Book title |
Bacterial Pathogenesis
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-6673-8_10 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6671-4, 978-1-4939-6673-8
|
| Authors |
Jonas Danielsen, Pontus Nordenfelt |
| Editors |
Pontus Nordenfelt, Mattias Collin |
| 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. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | 5% |
| Denmark | 1 | 5% |
| Unknown | 19 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 5 | 24% |
| Student > Master | 4 | 19% |
| Professor > Associate Professor | 3 | 14% |
| Librarian | 1 | 5% |
| Professor | 1 | 5% |
| Other | 3 | 14% |
| Unknown | 4 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 14% |
| Engineering | 3 | 14% |
| Agricultural and Biological Sciences | 2 | 10% |
| Immunology and Microbiology | 2 | 10% |
| Medicine and Dentistry | 2 | 10% |
| Other | 4 | 19% |
| Unknown | 5 | 24% |