| Title |
Quantitative Analysis for Breast Density Estimation in Low Dose Chest CT Scans
|
|---|---|
| Published in |
Journal of Medical Systems, March 2014
|
| DOI | 10.1007/s10916-014-0021-5 |
| Pubmed ID | |
| Authors |
Woo Kyung Moon, Chung-Ming Lo, Jin Mo Goo, Min Sun Bae, Jung Min Chang, Chiun-Sheng Huang, Jeon-Hor Chen, Violeta Ivanova, Ruey-Feng Chang |
| Abstract |
A computational method was developed for the measurement of breast density using chest computed tomography (CT) images and the correlation between that and mammographic density. Sixty-nine asymptomatic Asian women (138 breasts) were studied. With the marked lung area and pectoralis muscle line in a template slice, demons algorithm was applied to the consecutive CT slices for automatically generating the defined breast area. The breast area was then analyzed using fuzzy c-mean clustering to separate fibroglandular tissue from fat tissues. The fibroglandular clusters obtained from all CT slices were summed then divided by the summation of the total breast area to calculate the percent density for CT. The results were compared with the density estimated from mammographic images. For CT breast density, the coefficient of variations of intraoperator and interoperator measurement were 3.00 % (0.59 %-8.52 %) and 3.09 % (0.20 %-6.98 %), respectively. Breast density measured from CT (22 ± 0.6 %) was lower than that of mammography (34 ± 1.9 %) with Pearson correlation coefficient of r = 0.88. The results suggested that breast density measured from chest CT images correlated well with that from mammography. Reproducible 3D information on breast density can be obtained with the proposed CT-based quantification methods. |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 30 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 20% |
| Student > Master | 6 | 20% |
| Researcher | 4 | 13% |
| Student > Bachelor | 2 | 7% |
| Professor | 2 | 7% |
| Other | 4 | 13% |
| Unknown | 6 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 14 | 47% |
| Engineering | 2 | 7% |
| Computer Science | 1 | 3% |
| Economics, Econometrics and Finance | 1 | 3% |
| Agricultural and Biological Sciences | 1 | 3% |
| Other | 4 | 13% |
| Unknown | 7 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 07 May 2014.
All research outputs
#6,405,394
of 22,755,127 outputs
Outputs from Journal of Medical Systems
#220
of 1,144 outputs
Outputs of similar age
#61,486
of 223,396 outputs
Outputs of similar age from Journal of Medical Systems
#4
of 11 outputs
Altmetric has tracked 22,755,127 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,144 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 79% 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 223,396 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 11 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 63% of its contemporaries.