Saliency, Scale and Image Description
International Journal of Computer Vision
Ultrasonic speckle formation, analysis and processing applied to tissue characterization
Pattern Recognition Letters - Speciqal issue: Ultrasonic image processing and analysis
Iconic feature based nonrigid registration: the PASHA algorithm
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Deformable Ultrasound Registration without Reconstruction
MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Front-End Vision and Multi-Scale Image Analysis: Multi-scale Computer Vision Theory and Applications, written in Mathematica
Adaptive non-rigid registration of real time 3D ultrasound to cardiovascular MR images
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
A marginalized MAP approach and EM optimization for pair-wise registration
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Nonrigid image registration using conditional mutual information
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Maximum a posteriori local histogram estimation for image registration
MICCAI'05 Proceedings of the 8th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Multimodal registration via spatial-context mutual information
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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We propose a novel Bayesian registration formulation in which image location is represented as a latent random variable. Location is marginalized to determine the maximum a priori (MAP) transform between images, which results in registration that is more robust than the alternatives of omitting locality (i.e. global registration) or jointly maximizing locality and transform (i.e. iconic registration). A mathematical link is established between the Bayesian registration formulation and the mutual information (MI) similarity measure. This leads to a novel technique for selecting informative image regions for registration, based on the MI of image intensity and spatial location. Experimental results demonstrate the effectiveness of the marginalization formulation and the MI-based region selection technique for ultrasound (US) to magnetic resonance (MR) registration in an image-guided neurosurgical application.