Image super-resolution via sparse representation
IEEE Transactions on Image Processing
A generalized uncertainty principle and sparse representation in pairs of bases
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
Registration for correlative microscopy using image analogies
WBIR'12 Proceedings of the 5th international conference on Biomedical Image Registration
Hi-index | 0.00 |
The tissue contrast of a magnetic resonance (MR) neuroimaging data set has a major impact on image analysis tasks like registration and segmentation. It has been one of the core challenges of medical imaging to guarantee the consistency of these tasks regardless of the contrasts of the MR data. Inconsistencies in image analysis are attributable in part to variations in tissue contrast, which in turn arise from operator variations during image acquisition as well as software and hardware differences in the MR scanners. It is also a common problem that images with a desired tissue contrast are completely missing in a given data set for reasons of cost, acquisition time, forgetfulness, or patient comfort. Absence of this data can hamper the detailed, automatic analysis of some or all data sets in a scientific study. A method to synthesize missing MR tissue contrasts from available acquired images using an atlas containing the desired contrast and a patch-based compressed sensing strategy is described. An important application of this general approach is to synthesize a particular tissue contrast from multiple studies using a single atlas, thereby normalizing all data sets into a common intensity space. Experiments on real data, obtained using different scanners and pulse sequences, show improvement in segmentation consistency, which could be extremely valuable in the pooling of multi-site multiscanner neuroimaging studies.