In this paper a novel approach for the registration and segmentation of dynamic contrast enhanced renal MR images is presented. This integrated method is motivated by the observation of the reciprocity between registration and segmentation in 4D time-series images. Fully automated Fourier-based registration with sub-voxel accuracy and semi-automated time-series segmentation were intertwined to improve the accuracy in a multi-step fashion. We have tested our algorithm on several real patient data sets. Clinical validation showed remarkable and consistent agreement between the proposed method and manual segmentation by experts. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Song, T., Lee, V. S., Rusinek, H., Wong, S., & Laine, A. F. (2006). Integrated four dimensional registration and segmentation of dynamic renal MR images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4191 LNCS-II, pp. 758–765). Springer Verlag. https://doi.org/10.1007/11866763_93
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