Point-Based Rigid-Body Registration Using an Unscented Kalman Filter

TitlePoint-Based Rigid-Body Registration Using an Unscented Kalman Filter
Publication TypeJournal Article
Year of Publication2007
AuthorsMoghari, M. H., and P. Abolmaesumi
JournalMedical Imaging, IEEE Transactions on
Pagination1708 -1728
Date Publisheddec.
Keywords3-D meshes, additive Gaussian noise, Algorithms, Analysis of Variance, anisotropic Gaussian noise, Artifacts, Automated, AWGN, Biological, biomedical ultrasonics, bone, computed tomography, computer simulation, Computer-Assisted, computerised tomography, CT imaging, diagnostic radiography, feedback, Humans, image denoising, image processing, image registration, Imaging, isotropic Gaussian noise, Kalman filters, medical image processing, mesh generation, Models, Movement, nonlinear dynamics, nonlinear systems, Normal Distribution, orthopaedics, Pattern Recognition, Pelvic Bones, pelvic cadaver bone phantom, phantoms, point-based rigid-body registration, randomly generated data sets, regression analysis, rigid object, Scaphoid Bone, scaphoid bone phantom, Sensitivity and Specificity, Subtraction Technique, Three-Dimensional, tomography, tracked free-hand ultrasound imaging, Ultrasonography, unscented Kalman filter algorithm, X-Ray Computed

We present and validate a novel registration algorithm mapping two data sets, generated from a rigid object, in the presence of Gaussian noise. The proposed method is based on the unscented Kalman filter (UKF) algorithm that is generally employed for analyzing nonlinear systems corrupted by additive Gaussian noise. First, we employ our proposed registration algorithm to fit two randomly generated data sets in the presence of isotropic Gaussian noise, when the corresponding points between the two data sets are assumed to be known. Then, we extend the registration method to the case where the data (with known correspondences) is stimulated by anisotropic Gaussian noise. The new registration method not only reliably converges to the correct registration solution, but it also estimates the variance, as a confidence measure, for each of the estimated registration transformation parameters. Furthermore, we employ the proposed registration algorithm for rigid-body, point-based registration where corresponding points between two registering data sets are unknown. The algorithm is tested on point data sets which are garnered from a pelvic cadaver and a scaphoid bone phantom by means of computed tomography (CT) and tracked free-hand ultrasound imaging. The collected 3-D points in the ultrasound frame are registered to the 3-D meshes in the CT frame by using the proposed and the standard iterative closest points (ICP) registration algorithms. Experimental results demonstrate that our proposed method significantly outperforms the ICP registration algorithm in the presence of additive Gaussian noise. It is also shown that the proposed registration algorithm is more robust than the ICP registration algorithm in terms of outliers in data sets and initial misalignment between the two data sets.


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