Multiresolution elastic matching
Computer Vision, Graphics, and Image Processing
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Saliency, Scale and Image Description
International Journal of Computer Vision
Fast Fluid Registration of Medical Images
VBC '96 Proceedings of the 4th International Conference on Visualization in Biomedical Computing
MRF Solutions for Probabilistic Optical Flow Formulations
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Non-rigid image registration using graph-cuts
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
De-enhancing the dynamic contrast-enhanced breast MRI for robust registration
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Joint registration and segmentation of dynamic cardiac perfusion images using MRFs
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
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Nonrigid registration of contrast-enhanced MR images is a difficult problem due to the change in pixel intensity caused by the wash-in and wash-out of the contrast agent. In this paper we propose a novel saliency based Markov Random Field approach for effective nonrigid registration of contrast enhanced images. Saliency information obtained from the neurobiology-based saliency model alongwith intensity information is used to quantify the degree of similarity between images in the pre- and post-contrast stages. Information from these two features is combined by using an exponential function of the saliency difference such that it assigns low values to small differences in saliency and at the same time ensures that saliency information does not bias the energy term. Rotationally-invariant edge information from edge-orientation histograms was used to complement the saliency information resulting in better registration results. Tests on real patient datasets show that our algorithm results in accurate registration. We also simulated elastic motion on images, and the deformation field recovered by our algorithm was nearly the inverse of the simulated field.