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Bacterial cell identification in differential interference contrast microscopy images

Overview of attention for article published in BMC Bioinformatics, April 2013
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1 tweeter

Citations

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Readers on

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65 Mendeley
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Title
Bacterial cell identification in differential interference contrast microscopy images
Published in
BMC Bioinformatics, April 2013
DOI 10.1186/1471-2105-14-134
Pubmed ID
Authors

Boguslaw Obara, Mark AJ Roberts, Judith P Armitage, Vicente Grau

Abstract

Microscopy image segmentation lays the foundation for shape analysis, motion tracking, and classification of biological objects. Despite its importance, automated segmentation remains challenging for several widely used non-fluorescence, interference-based microscopy imaging modalities. For example in differential interference contrast microscopy which plays an important role in modern bacterial cell biology. Therefore, new revolutions in the field require the development of tools, technologies and work-flows to extract and exploit information from interference-based imaging data so as to achieve new fundamental biological insights and understanding.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 3%
India 1 2%
Germany 1 2%
Portugal 1 2%
Belgium 1 2%
United States 1 2%
Unknown 58 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 29%
Researcher 10 15%
Student > Bachelor 10 15%
Student > Master 10 15%
Student > Postgraduate 3 5%
Other 10 15%
Unknown 3 5%
Readers by discipline Count As %
Engineering 14 22%
Agricultural and Biological Sciences 14 22%
Biochemistry, Genetics and Molecular Biology 8 12%
Computer Science 5 8%
Chemistry 4 6%
Other 14 22%
Unknown 6 9%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 April 2013.
All research outputs
#8,872,669
of 11,126,160 outputs
Outputs from BMC Bioinformatics
#3,506
of 4,185 outputs
Outputs of similar age
#87,807
of 128,206 outputs
Outputs of similar age from BMC Bioinformatics
#109
of 123 outputs
Altmetric has tracked 11,126,160 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,185 research outputs from this source. They receive a mean Attention Score of 5.0. This one is in the 7th percentile – i.e., 7% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 128,206 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 123 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.