TY - GEN
T1 - Registration of DCE mr images for computer-aided diagnosis of breast cancer
AU - Wu, Qiu
AU - Whitman, Gary J.
AU - Fussell, Donald S.
AU - Markey, Mia K.
PY - 2006
Y1 - 2006
N2 - The kinetic features of lesions on dynamic contrast-enhanced (DCE) breast MRI provide important diagnostic information. However, the same coordinates in raw DCE breast MR images at different times in the series may correspond to different physical locations in the subject due to respiratory motion, cardiac motion, and patient movements during image acquisition. In order to extract accurate kinetic features, an image registration step is necessary to spatially align the voxels across sequentially collected breast MR image volumes to ensure accurate time curve signal representation at each spatial location of the lesion. The challenges in registering DCE breast MR images are that the breasts undergo non-rigid motion and that the image intensity changes over time. This paper presents a registration scheme that employs an elastic model as the deformation model and normalized cross correlation as the similarity term. Symmetric consistency is used to evaluate the registration algorithm. Our results indicate that a local similarity metric such as normalized cross correlation can achieve desirable registration performance.
AB - The kinetic features of lesions on dynamic contrast-enhanced (DCE) breast MRI provide important diagnostic information. However, the same coordinates in raw DCE breast MR images at different times in the series may correspond to different physical locations in the subject due to respiratory motion, cardiac motion, and patient movements during image acquisition. In order to extract accurate kinetic features, an image registration step is necessary to spatially align the voxels across sequentially collected breast MR image volumes to ensure accurate time curve signal representation at each spatial location of the lesion. The challenges in registering DCE breast MR images are that the breasts undergo non-rigid motion and that the image intensity changes over time. This paper presents a registration scheme that employs an elastic model as the deformation model and normalized cross correlation as the similarity term. Symmetric consistency is used to evaluate the registration algorithm. Our results indicate that a local similarity metric such as normalized cross correlation can achieve desirable registration performance.
UR - http://www.scopus.com/inward/record.url?scp=47049084581&partnerID=8YFLogxK
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U2 - 10.1109/ACSSC.2006.354865
DO - 10.1109/ACSSC.2006.354865
M3 - Conference contribution
AN - SCOPUS:47049084581
SN - 1424407850
SN - 9781424407859
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 826
EP - 830
BT - Conference Record of the 40th Asilomar Conference on Signals, Systems and Computers, ACSSC '06
T2 - 40th Asilomar Conference on Signals, Systems, and Computers, ACSSC '06
Y2 - 29 October 2006 through 1 November 2006
ER -