↓ Skip to main content

Tracing retinal vessel trees by transductive inference

Overview of attention for article published in BMC Bioinformatics, January 2014
Altmetric Badge

About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Above-average Attention Score compared to outputs of the same age and source (51st percentile)

Mentioned by

twitter
5 tweeters

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
58 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
Tracing retinal vessel trees by transductive inference
Published in
BMC Bioinformatics, January 2014
DOI 10.1186/1471-2105-15-20
Pubmed ID
Authors

Jaydeep De, Huiqi Li, Li Cheng

Abstract

Structural study of retinal blood vessels provides an early indication of diseases such as diabetic retinopathy, glaucoma, and hypertensive retinopathy. These studies require accurate tracing of retinal vessel tree structure from fundus images in an automated manner. However, the existing work encounters great difficulties when dealing with the crossover issue commonly-seen in vessel networks.

Twitter Demographics

The data shown below were collected from the profiles of 5 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 58 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 17%
Researcher 9 16%
Student > Doctoral Student 7 12%
Student > Master 6 10%
Professor 4 7%
Other 7 12%
Unknown 15 26%
Readers by discipline Count As %
Computer Science 20 34%
Engineering 11 19%
Medicine and Dentistry 8 14%
Agricultural and Biological Sciences 2 3%
Biochemistry, Genetics and Molecular Biology 1 2%
Other 1 2%
Unknown 15 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 17 September 2014.
All research outputs
#12,891,407
of 22,739,983 outputs
Outputs from BMC Bioinformatics
#3,786
of 7,267 outputs
Outputs of similar age
#154,877
of 304,982 outputs
Outputs of similar age from BMC Bioinformatics
#47
of 99 outputs
Altmetric has tracked 22,739,983 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,267 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 45th percentile – i.e., 45% 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 304,982 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.