Abstract
Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T 1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T 1 map estimation (E-Step) and the step of T 1 map based tissue segmentation (S-Step) can benefit each other. Since the estimated T 1 map can be noisy due to the complexity of T 1 estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T 1 map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T 1 map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T 1 map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement.
| Original language | English |
|---|---|
| Title of host publication | 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings |
| Pages | 332-335 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2007 Nov 27 |
| Externally published | Yes |
| Event | 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 - Arlington, VA, United States Duration: 2007 Apr 12 → 2007 Apr 15 |
Other
| Other | 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07 |
|---|---|
| Country | United States |
| City | Arlington, VA |
| Period | 07/4/12 → 07/4/15 |
Fingerprint
Keywords
- Breast cancer
- Estimation
- Image segmentation
- Parenchyma
- T map
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Medicine(all)
Cite this
Simultaneous estimation and segmentation of T 1 map for breast parenchyma measurement. / Ye, Xing; Yangming, Ou; Englander, Sarah; Schnall, Mitchell; Shen, Dinggang.
2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings. 2007. p. 332-335 4193290.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Simultaneous estimation and segmentation of T 1 map for breast parenchyma measurement
AU - Ye, Xing
AU - Yangming, Ou
AU - Englander, Sarah
AU - Schnall, Mitchell
AU - Shen, Dinggang
PY - 2007/11/27
Y1 - 2007/11/27
N2 - Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T 1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T 1 map estimation (E-Step) and the step of T 1 map based tissue segmentation (S-Step) can benefit each other. Since the estimated T 1 map can be noisy due to the complexity of T 1 estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T 1 map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T 1 map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T 1 map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement.
AB - Breast density has been shown to be an independent risk factor for breast cancer. In order to segment breast parenchyma, which has been proposed as a biomarker of breast cancer risk, we present an integrated algorithm for simultaneous T 1 map estimation and segmentation, using a series of magnetic resonance (MR) breast images. The advantage of using this algorithm is that the step of T 1 map estimation (E-Step) and the step of T 1 map based tissue segmentation (S-Step) can benefit each other. Since the estimated T 1 map can be noisy due to the complexity of T 1 estimation method, the tentative tissue segmentation results from S-Step can help perform the edge-preserving smoothing on the estimated T 1 map in E-Step, thus removing noises and also preserving tissue boundaries. On the other hand, the improved estimation of T 1 map from E-Step can help segment breast tissues in a more accurate and less noisy way. Therefore, by repeating these steps, we can simultaneously obtain better results for both T 1 map estimation and segmentation. Experimental results show the effectiveness of the proposed algorithm in breast tissue segmentation and parenchyma volume measurement.
KW - Breast cancer
KW - Estimation
KW - Image segmentation
KW - Parenchyma
KW - T map
UR - http://www.scopus.com/inward/record.url?scp=36348949109&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36348949109&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2007.356856
DO - 10.1109/ISBI.2007.356856
M3 - Conference contribution
AN - SCOPUS:36348949109
SN - 1424406722
SN - 9781424406722
SP - 332
EP - 335
BT - 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings
ER -