| Title |
Unified wavelet and gaussian filtering for segmentation of CT images; application in segmentation of bone in pelvic CT images
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, November 2009
|
| DOI | 10.1186/1472-6947-9-s1-s8 |
| Pubmed ID | |
| Authors |
Simina Vasilache, Kevin Ward, Charles Cockrell, Jonathan Ha, Kayvan Najarian |
| Abstract |
The analysis of pelvic CT scans is a crucial step for detecting and assessing the severity of Traumatic Pelvic Injuries. Automating the processing of pelvic CT scans could impact decision accuracy, decrease the time for decision making, and reduce health care cost. This paper discusses a method to automate the segmentation of bone from pelvic CT images. Accurate segmentation of bone is very important for developing an automated assisted-decision support system for Traumatic Pelvic Injury diagnosis and treatment. |
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 % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Indonesia | 1 | 3% |
| United Kingdom | 1 | 3% |
| United States | 1 | 3% |
| India | 1 | 3% |
| Unknown | 33 | 89% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 32% |
| Student > Master | 6 | 16% |
| Lecturer | 3 | 8% |
| Student > Doctoral Student | 3 | 8% |
| Student > Bachelor | 3 | 8% |
| Other | 3 | 8% |
| Unknown | 7 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 8 | 22% |
| Engineering | 7 | 19% |
| Computer Science | 6 | 16% |
| Nursing and Health Professions | 1 | 3% |
| Psychology | 1 | 3% |
| Other | 5 | 14% |
| Unknown | 9 | 24% |
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 21 March 2014.
All research outputs
#18,367,612
of 22,749,166 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,567
of 1,985 outputs
Outputs of similar age
#86,207
of 94,418 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#3
of 5 outputs
Altmetric has tracked 22,749,166 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 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 9th percentile – i.e., 9% 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 94,418 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.