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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Segmentation of epidermal tissue with histopathological damage in images of haematoxylin and eosin stained human skin
|
|---|---|
| Published in |
BMC Medical Imaging, February 2014
|
| DOI | 10.1186/1471-2342-14-7 |
| Pubmed ID | |
| Authors |
Juliana M Haggerty, Xiao N Wang, Anne Dickinson, Chris J O’Malley, Elaine B Martin |
| Abstract |
Digital image analysis has the potential to address issues surrounding traditional histological techniques including a lack of objectivity and high variability, through the application of quantitative analysis. A key initial step in image analysis is the identification of regions of interest. A widely applied methodology is that of segmentation. This paper proposes the application of image analysis techniques to segment skin tissue with varying degrees of histopathological damage. The segmentation of human tissue is challenging as a consequence of the complexity of the tissue structures and inconsistencies in tissue preparation, hence there is a need for a new robust method with the capability to handle the additional challenges materialising from histopathological damage. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 50% |
| Brazil | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Finland | 1 | 2% |
| United Kingdom | 1 | 2% |
| Unknown | 59 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 13 | 21% |
| Student > Master | 11 | 18% |
| Student > Ph. D. Student | 7 | 11% |
| Researcher | 6 | 10% |
| Student > Doctoral Student | 5 | 8% |
| Other | 4 | 7% |
| Unknown | 15 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 12 | 20% |
| Medicine and Dentistry | 10 | 16% |
| Computer Science | 7 | 11% |
| Agricultural and Biological Sciences | 4 | 7% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Other | 9 | 15% |
| Unknown | 16 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 February 2018.
All research outputs
#4,415,452
of 22,743,667 outputs
Outputs from BMC Medical Imaging
#47
of 590 outputs
Outputs of similar age
#55,482
of 313,178 outputs
Outputs of similar age from BMC Medical Imaging
#2
of 13 outputs
Altmetric has tracked 22,743,667 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 590 research outputs from this source. They receive a mean Attention Score of 2.1. This one has done particularly well, scoring higher than 92% 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 313,178 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 82% of its contemporaries.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.