2009
DOI: 10.1002/cyto.a.20810
|View full text |Cite
|
Sign up to set email alerts
|

Automated quality assessment of autonomously acquired microscopic images of fluorescently stained bacteria

Abstract: Quality assessment of autonomously acquired microscopic images is an important issue in high-throughput imaging systems. For example, the presence of low quality images (!10%) in a dataset significantly influences the counting precision of fluorescently stained bacterial cells. We present an approach based on an artificial neural network (ANN) to assess the quality of such images. Spatially invariant estimators were extracted as ANN input data from subdivided images by low level image processing. Different ANN… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
26
0

Year Published

2009
2009
2023
2023

Publication Types

Select...
8

Relationship

0
8

Authors

Journals

citations
Cited by 26 publications
(26 citation statements)
references
References 26 publications
0
26
0
Order By: Relevance
“…S1G in the supplemental material). For each channel, the exposure time (constant or variable), focusing procedure, and number of images per z-stack for the compensation of filter unevenness were selected (20). Notably, for each channel, only one exposure time, either constant or variable, could be selected.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…S1G in the supplemental material). For each channel, the exposure time (constant or variable), focusing procedure, and number of images per z-stack for the compensation of filter unevenness were selected (20). Notably, for each channel, only one exposure time, either constant or variable, could be selected.…”
Section: Methodsmentioning
confidence: 99%
“…S1H in the supplemental material), and low-quality images, such as images with over-or underexposed parts, areas out of focus (unevenness), or too many aggregates, large phytoplankton cells, debris, and particles, were excluded from further analysis to ensure high data quality (see Fig. S3 to S6 in the supplemental material) (20). In a second step, a so-called metafile was calculated from the remaining HQ images for faster image processing (see Fig.…”
Section: Methodsmentioning
confidence: 99%
“…In Zeder et al (2010), a unary-class classification approach is introduced to distinguish fluorescently stained bacteria, where spatially invariant estimators are used as features to represent the SMs first, and then an ANN classifier is designed. In the experiment, 25,000 microscopic images are applied to evaluate the classification performance, and finally a classification accuracy of 94% is obtained.…”
Section: Overview Of Sm Classificationmentioning
confidence: 99%
“…An experimentalist should therefore keep environmental conditions as constant as possible. Data quality and reproducibility can be assessed by automated quality control (Zeder et al, 2010) and by incorporating control treatments in the assay. Differences in image features resulting from experimental variations are unlikely to be become obvious in the evaluation of the machine-learning method itself and thus have to be avoided early on in data acquisition and sample preparation.…”
Section: Some Experimental Design Guidelinesmentioning
confidence: 99%