Imperial College London


Faculty of EngineeringDepartment of Computing

Head of Department of Computing



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BibTex format

author = {Caballero, J and Bai, W and Price, AN and Rueckert, D and Hajnal, JV},
doi = {10.1007/978-3-319-10404-1_14},
journal = {Med Image Comput Comput Assist Interv},
pages = {106--113},
title = {Application-driven MRI: joint reconstruction and segmentation from undersampled MRI data.},
url = {},
volume = {17},
year = {2014}

RIS format (EndNote, RefMan)

AB - 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.
AU - Caballero,J
AU - Bai,W
AU - Price,AN
AU - Rueckert,D
AU - Hajnal,JV
DO - 10.1007/978-3-319-10404-1_14
EP - 113
PY - 2014///
SP - 106
TI - Application-driven MRI: joint reconstruction and segmentation from undersampled MRI data.
T2 - Med Image Comput Comput Assist Interv
UR -
UR -
VL - 17
ER -