TY - GEN
T1 - De-enhancing the dynamic contrast-enhanced breast MRI for robust registration
AU - Zheng, Yuanjie
AU - Yu, Jingyi
AU - Kambhamettu, Chandra
AU - Englander, Sarah
AU - Schnall, Mitchell D.
AU - Shen, Dinggang
PY - 2007
Y1 - 2007
N2 - Dynamic enhancement causes serious problems for registration of contrast enhanced breast MRI, due to variable uptakes of agent on different tissues or even same tissues in the breast. We present an iterative optimization algorithm to de-enhance the dynamic contrastenhanced breast MRI and then register them for avoiding the effects of enhancement on image registration. In particular, the spatially varying enhancements are modeled by a Markov Random Field, and estimated by a locally smooth function with boundaries using a graph cut algorithm. The de-enhanced images are then registered by conventional Bspline based registration algorithm. These two steps benefit from each other and are repeated until the results converge. Experimental results show that our two-step registration algorithm performs much better than conventional mutual information based registration algorithm. Also, the effects of tumor shrinking in the conventional registration algorithms can be effectively avoided by our registration algorithm.
AB - Dynamic enhancement causes serious problems for registration of contrast enhanced breast MRI, due to variable uptakes of agent on different tissues or even same tissues in the breast. We present an iterative optimization algorithm to de-enhance the dynamic contrastenhanced breast MRI and then register them for avoiding the effects of enhancement on image registration. In particular, the spatially varying enhancements are modeled by a Markov Random Field, and estimated by a locally smooth function with boundaries using a graph cut algorithm. The de-enhanced images are then registered by conventional Bspline based registration algorithm. These two steps benefit from each other and are repeated until the results converge. Experimental results show that our two-step registration algorithm performs much better than conventional mutual information based registration algorithm. Also, the effects of tumor shrinking in the conventional registration algorithms can be effectively avoided by our registration algorithm.
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U2 - 10.1007/978-3-540-75757-3_113
DO - 10.1007/978-3-540-75757-3_113
M3 - Conference contribution
C2 - 18051148
AN - SCOPUS:79551687077
SN - 9783540757566
VL - 4791 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 933
EP - 941
BT - Medical Image Computing and Computer-Assisted Intervention - 10th International Conference, Proceedings
PB - Springer Verlag
T2 - 10th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2007
Y2 - 29 October 2007 through 2 November 2007
ER -