Journal Article10.1016/J.MEDIA.2010.03.005
A review of 3D/2D registration methods for image-guided interventions
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TL;DR: The 3D/2D registration methods are reviewed with respect to image modality, image dimensionality, registration basis, geometric transformation, user interaction, optimization procedure, subject, and object of registration.
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About: This article is published in Medical Image Analysis. The article was published on 01 Apr 2012. The article focuses on the topics: Patient registration & Image registration.
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Citations
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TL;DR: A Convolutional Neural Network regression approach for real-time 2-D/3-D registration that exploits the information embedded in the appearances of the Digitally Reconstructed Radiograph and X-ray images and employs CNN regressors to directly estimate the transformation parameters.
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References
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TL;DR: In this paper, the authors describe a general-purpose representation-independent method for the accurate and computationally efficient registration of 3D shapes including free-form curves and surfaces, based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point.
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Multimodality image registration by maximization of mutual information
TL;DR: The results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.
Light field rendering
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TL;DR: This paper describes a sampled representation for light fields that allows for both efficient creation and display of inward and outward looking views, and describes a compression system that is able to compress the light fields generated by more than a factor of 100:1 with very little loss of fidelity.
Closed-form solution of absolute orientation using unit quaternions
TL;DR: A closed-form solution to the least-squares problem for three or more paints is presented, simplified by use of unit quaternions to represent rotation.
Least-Squares Fitting of Two 3-D Point Sets
TL;DR: An algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix, is presented.