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 - Dinggang, Shen
PY - 2007
Y1 - 2007
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
U2 - 10.1109/ISBI.2007.356856
DO - 10.1109/ISBI.2007.356856
M3 - Conference contribution
AN - SCOPUS:36348949109
SN - 1424406722
SN - 9781424406722
T3 - 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro - Proceedings
SP - 332
EP - 335
BT - 2007 4th IEEE International Symposium on Biomedical Imaging
T2 - 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro; ISBI'07
Y2 - 12 April 2007 through 15 April 2007
ER -