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- W150961595 abstract "Medical image segmentation has traditionally been regarded as a separate process from image acquisition and reconstruction, even though its performance directly depends on the quality and characteristics of these first stages of the imaging pipeline. Adopting an integrated acquisition-reconstruction-segmentation process can provide a more efficient and accurate solution. In this paper we propose a joint segmentation and reconstruction algorithm for undersampled magnetic resonance data. Merging a reconstructive patch-based sparse modelling and a discriminative Gaussian mixture modelling can produce images with enhanced edge information ultimately improving their segmentation." @default.
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- W150961595 date "2014-01-01" @default.
- W150961595 modified "2023-10-16" @default.
- W150961595 title "Application-Driven MRI: Joint Reconstruction and Segmentation from Undersampled MRI Data" @default.
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- W150961595 doi "https://doi.org/10.1007/978-3-319-10404-1_14" @default.
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