Abstract
Breast Contrast-Enhanced MRI (ce-MRI) requires a series of images to be acquired before, and repeatedly after, intravenous injection of a contrast agent. Breast MRI segmentation based on the differential enhancement of image intensities can assist the clinician detect suspicious regions. Image registration between the temporal data sets is necessary to compensate for patient motion, which is quite often substantial. Although segmentation and registration are usually treated as separate problems in medical image analysis, they can naturally benefit a great deal from each other. In this paper, we propose a scheme for simultaneous segmentation and registration of breast ce-MRI. It is developed within a Bayesian framework, based on a maximum a posteriori estimation method. A pharmacokinetic model and Markov Random Field model have been incorporated into the framework in order to improve the performance of our algorithm. Our method has been applied to the segmentation and registration of clinical ce-MR images. The results show the potential of our methodology to extract useful information for breast cancer detection.
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© 2005 Springer-Verlag Berlin Heidelberg
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Xiaohua, C., Brady, M., Lo, J.LC., Moore, N. (2005). Simultaneous Segmentation and Registration of Contrast-Enhanced Breast MRI. In: Christensen, G.E., Sonka, M. (eds) Information Processing in Medical Imaging. IPMI 2005. Lecture Notes in Computer Science, vol 3565. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11505730_11
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DOI: https://doi.org/10.1007/11505730_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26545-0
Online ISBN: 978-3-540-31676-3
eBook Packages: Computer ScienceComputer Science (R0)