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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A shape-optimized framework for kidney segmentation in ultrasound images using NLTV denoising and DRLSE
|
|---|---|
| Published in |
BioMedical Engineering OnLine, October 2012
|
| DOI | 10.1186/1475-925x-11-82 |
| Pubmed ID | |
| Authors |
Fan Yang, Wenjian Qin, Yaoqin Xie, Tiexiang Wen, Jia Gu |
| Abstract |
Computer-assisted surgical navigation aims to provide surgeons with anatomical target localization and critical structure observation, where medical image processing methods such as segmentation, registration and visualization play a critical role. Percutaneous renal intervention plays an important role in several minimally-invasive surgeries of kidney, such as Percutaneous Nephrolithotomy (PCNL) and Radio-Frequency Ablation (RFA) of kidney tumors, which refers to a surgical procedure where access to a target inside the kidney by a needle puncture of the skin. Thus, kidney segmentation is a key step in developing any ultrasound-based computer-aided diagnosis systems for percutaneous renal intervention. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 3% |
| Unknown | 39 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 25% |
| Student > Master | 7 | 18% |
| Student > Doctoral Student | 4 | 10% |
| Researcher | 4 | 10% |
| Student > Postgraduate | 4 | 10% |
| Other | 4 | 10% |
| Unknown | 7 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 12 | 30% |
| Medicine and Dentistry | 9 | 23% |
| Computer Science | 8 | 20% |
| Agricultural and Biological Sciences | 1 | 3% |
| Unknown | 10 | 25% |
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 30 October 2012.
All research outputs
#17,670,096
of 22,684,168 outputs
Outputs from BioMedical Engineering OnLine
#529
of 821 outputs
Outputs of similar age
#134,529
of 183,634 outputs
Outputs of similar age from BioMedical Engineering OnLine
#5
of 18 outputs
Altmetric has tracked 22,684,168 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 821 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 31st percentile – i.e., 31% 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 183,634 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 33rd percentile – i.e., 33% of its contemporaries scored the same or lower than it.