↓ Skip to main content

Unsupervised segmentation of noisy electron microscopy images using salient watersheds and region merging

Overview of attention for article published in BMC Bioinformatics, October 2013
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (85th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

blogs
1 blog
twitter
2 tweeters

Citations

dimensions_citation
9 Dimensions

Readers on

mendeley
36 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Unsupervised segmentation of noisy electron microscopy images using salient watersheds and region merging
Published in
BMC Bioinformatics, October 2013
DOI 10.1186/1471-2105-14-294
Authors

Saket Navlakha, Parvez Ahammad, Eugene W Myers

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Germany 1 3%
Unknown 34 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 25%
Researcher 8 22%
Student > Master 6 17%
Student > Doctoral Student 4 11%
Librarian 1 3%
Other 2 6%
Unknown 6 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 25%
Computer Science 8 22%
Engineering 5 14%
Physics and Astronomy 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 3 8%
Unknown 7 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 15 June 2014.
All research outputs
#492,271
of 4,504,945 outputs
Outputs from BMC Bioinformatics
#359
of 2,646 outputs
Outputs of similar age
#14,088
of 100,309 outputs
Outputs of similar age from BMC Bioinformatics
#18
of 92 outputs
Altmetric has tracked 4,504,945 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,646 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done well, scoring higher than 86% of its peers.
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 100,309 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 85% of its contemporaries.
We're also able to compare this research output to 92 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.