An efficient motion estimator with application to medical image registration

Department of Computer & Information Sciences & Engineering, University of Florida, Gainesville 32611, USA.
Medical Image Analysis (Impact Factor: 3.65). 04/1998; 2(1):79-98. DOI: 10.1016/S1361-8415(01)80029-3
Source: PubMed


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 of a patient 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 a hierarchical 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 the Hessian of the energy function at the optimum without explicitly knowing the optimum. This idea is used for both global and local motion estimation in the hierarchical motion model. We present examples of motion estimation on synthetic and real data (from a patient acquired during pre- and post-operative stages) and compare the performance of our algorithm with that of competing ones.

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Available from: Sartaj Sahni, Oct 07, 2014
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    • "The work done by Nguyen and la Torre has similar set-up as ours while the main difference is in the specific learning approach being employed. Note that image alignment problem is in the context of registering between one image and a superviselylearned model, which is different to the conventional image registration/tracking problem being solved between two images [10], [24], [26]. "
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    • "In some cases the Hessian is approximated as constant [7] [4] [14], so that it can be precomputed , which in turn leads to particularly efficient fitting algorithms . The state of the art is often considered to be the Inverse Compositional Image Alignment (ICIA) method of Matthews and Baker [9] [1] [2]. "
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    Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on; 07/2009
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    • "The purpose of image registration is to spatially align two or more single-modality images taken at different times, or several images acquired by multiple imaging modalities [1] [2] [3] [4] [5]. Image registration techniques can be generally classified into two classes [1], namely intensity-based techniques and feature-based techniques. "
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