A new information-theoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative pose until the mutual information between images is maximized. In our derivation of the registration procedure, few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and can foreseeably be used with a wide variety of imaging devices. This approach works directly with raw images; no preprocessing or feature detection is required. As opposed to feature-based techniques, all of the information in the scan is used to evaluate the registration. This technique is however more flexible and robust than other intensity based techniques like correlation. Additionally, it has an efficient implementation that is based on stochastic approximation. Experiments are presented that demonstrate the approach registering magnetic resonance (MR) images with comput...

In this paper, we propose and analyze several methods to estimate a rigid transformation from a set of 3-D matched points or matched frames, which are important features in geometric algorithms. We also develop tools to predict and verify the accuracy of these estimations. The theoretical contributions are: an intrinsic model of noise for transformations based on composition rather than addition; a unified formalism for the estimation of both the rigid transformation and its covariance matrix for points or frames correspondences, and a statistical validation method to verify the error estimation, which applies even when no "ground truth" is available. We analyze and demonstrate on synthetic data that our scheme is well behaved. The practical contribution of the paper is the validation of our transformation estimation method in the case of 3-D medical images, which shows that an accuracy of the registration far below the size of a voxel can be achieved, and in the case of protein substructure matching, where frame features drastically improve both selectivity and complexity. 1.

Image registration is a very important problem in computer vision and medical image processing. Numerous algorithms for registering multi-modal image data have been reported in these areas. Robustness as well as computational efficiency are prime factors of importance in image data registration. In this paper, a robust and efficient algorithm for estimating the transformation between two image data sets is presented. Estimating the registration between two image data sets is formulated as a motion estimation problem. We use an optical flow motion model which allows for both global as well as local motion between the data sets. In this hierarchical motion model, we represent the flow field with a B-spline basis which implicitly incorporates smoothness constraints on the field. In computing the motion, we minimize the expectation of the squared differences energy function numerically via a modified Newton iteration scheme. The main idea in the modified Newton method is that we precompute...

Image registration is a very important problem in computer vision and medical image processing. Numerous algorithms for registering single and multi-modal image data have been reported in these areas. Robustness as well as computational efficiency are prime factors of importance in image data registration. In this paper, a robust/reliable and efficient algorithm for estimating the transformation between two image data sets taken from the same modality over time is presented. Estimating the registration between two image data sets is formulated as a motion estimation problem. We use an optical flow motion model which allows for both global as well as local motion between the data sets. In this