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ABSTRACT: Image registration is a single point of failure in the image-guided computer-assisted surgery. Registration is primarily used to align and fuse the data sets taken from patient's anatomy before and during surgeries. Point-based rigid-body registration is usually performed by identifying corresponding fiducials (either natural landmarks or implanted ones) in the data sets. Since the localization of fiducials is imprecise and is generally perturbed by random noise, the performed registration is imperfect and has some error. Previous work has extensively analyzed the behavior of this error when the fiducial localization error has zero-mean over the entire set of fiducials. However, if noise has a nonzero-mean or a bias, no formulation yet exists to determine the effect of noise on the overall registration accuracy. In this work, we derive novel formulations that relate the bias in the localized fiducials to the accuracy of the performed registration. We analytically and numerically demonstrate that by eliminating the estimated bias from the measured fiducial locations, one can effectively increase the accuracy of the performed registration.
IEEE transactions on medical imaging. 10/2010; 29(10):1730-8.
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IEEE Trans. Med. Imaging. 01/2010; 29:708-723.
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IEEE Trans. Med. Imaging. 01/2010; 29:1730-1738.
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ABSTRACT: Rigid-body point-based registration is frequently used in computer assisted surgery to align corresponding points, or fiducials, in preoperative and intraoperative data. This alignment is mostly achieved by assuming the same homogeneous error distribution for all the points; however, due to the properties of the medical instruments used in measuring the point coordinates, the error distribution might be inhomogeneous and different for each point. In this paper, in an effort to understand the effect of error distribution in the localized points on the performed registration, we derive a closed-form solution relating the error distribution of each point with the performed registration accuracy. The solution uses maximum likelihood estimation to calculate the probability density function of registration error at each fiducial point. Extensive numerical simulations are performed to validated the proposed solution.
IEEE transactions on medical imaging. 11/2009; 28(11):1791-801.
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ABSTRACT: In point-based rigid-body registration, target registration error (TRE) is an important measure of the accuracy of the performed registration. The registration's accuracy depends on the fiducial localization error (FLE) which, in turn, is due to the measurement errors in the points (fiducials) used to perform the registration. FLE may have different characteristics and distributions at each point of the registering data sets, and along each orthogonal axis. Previously, the distribution of TRE was estimated based on the assumption that FLE has an independent, identical, and isotropic or anisotropic distribution for each point in the registering data sets. In this article, we present a general solution based on the Maximum Likelihood (ML) algorithm that estimates the distribution of TRE for the cases where FLE has an independent, identical or inhomogeneous, isotropic or anisotropic, distribution at each point in the registering data sets, and when an algorithm is available that is capable of calculating the optimum registration to first order. Mathematically, we show that the proposed algorithm simplifies to the one proposed by Fitzpatrick and West when FLE has an independent, identical, and isotropic distribution in the registering data sets. Furthermore, we use numerical simulations to show that the proposed algorithm accurately estimates the distribution of TRE when FLE has an independent, inhomogeneous, and anisotropic distribution in the registering data sets.
IEEE transactions on medical imaging. 06/2009; 28(6):799-813.
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IEEE Trans. Med. Imaging. 01/2009; 28:799-813.
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ABSTRACT: We propose a new technique to automatically register multiple bone fragments of a fracture using a global registration method guided by a statistical anatomical atlas model. The atlas is created by using the principal component analysis of three-dimensional meshes generated from the Computed Tomography (CT) data of five left femur bone cadavers. The fracture fragments are initially aligned to the generated atlas by using a local point descriptor which can robustly identify a set of potential corresponding points among fragments and the mean atlas model. A global registration algorithm is then utilized to fine-tune the alignment among the fractures and the mean atlas model. Performed experimental simulations on the human femur bone cadavers verify the feasibility of the proposed registration algorithm.
Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE; 09/2008
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ABSTRACT: Estimation of target registration error (TRE), a common measure of the registration accuracy, is an important issue in computer assisted surgeries. Within the last decade, several new approaches have been developed to estimate either the mean squared value of TRE or the distribution of TRE under different noise conditions. In this paper, we theoretically demonstrate that all the proposed algorithms converge to a general Maximum Likelihood (ML) solution. Numerical simulations are performed to validate our derivations. Using experimentally measured fiducial localization error, we provide an example of TRE prediction in the presence of anisotropic noise.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2008; 11(Pt 2):1032-40.
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ABSTRACT: 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.
IEEE Transactions on Medical Imaging 01/2008; 26(12):1708-28. · 3.64 Impact Factor
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ABSTRACT: An algorithm to globally register multiple 3D data sets (point sets) within a general reference frame is proposed. The algorithm uses the Unscented Kalman Filter algorithm to simultaneously compute the registration transformations that map the data sets together, and to calculate the variances of the registration parameters. The data sets are either randomly generated, or collected from a set of fractured bone phantoms using Computed Tomography (CT) images. The algorithm robustly converges for isotropic Gaussian noise that could have perturbed the point coordinates in the data sets. It is also computationally efficient, and enables real-time global registration of multiple data sets, with applications in computer-assisted orthopaedic trauma surgery.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2007; 10(Pt 2):943-50.
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Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part II; 01/2007
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ABSTRACT: Rigid registration of pre-operative surgical plans to intraoperative coordinates of a patient is an important step in computer-assisted orthopaedic surgery. A good measure for registration accuracy is the target registration error (TRE) which is the distance after registration between a pair of corresponding points not used in the registration process. However, TRE is not a deterministic value, since there is always error in the localized features (points) utilized in the registration. In this situation, the distribution of TRE carries more information than TRE by itself. Previously, the distribution of TRE has been estimated with the accuracy of the first-order approximation. In this paper, we analytically approximate the TRE distribution up to at least the second-order accuracy based on the Unscented Kalman Filter algorithm.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2006; 9(Pt 2):603-11.
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ABSTRACT: Rigid registration is a crucial step in guidance system designed for neurosurgery, hip surgery, spine surgery and orthopaedic surgery. These systems often rely on point-based registration to determine the rigid transformation. The points used for registration (fiducial points) can be either extracted from the object being registered or created by implanting fiducial markers on the object. The localized fiducial points are generally corrupted by the noise which is called fiducial (point) localization error. In this work, we present a new point-based registration algorithm based on the Unscented Kalman Filter (UKF) algorithm and compare it with the earlier proposed registration algorithm which is based on the Extended Kalman Filter (EKF) algorithm. By means of numerical simulations, it is shown that the UKF registration algorithm more accurately estimates the registration parameters than the EKF registration algorithm. In addition, in contrast with EKF, UKF computes the variance of the estimated registration parameters with the accuracy of at least second-order Taylor series expansion. The computed variances are valuable information that can be used to determine the accuracy of the registration at any desired target positions (target registration error). We utilize the estimated variance of the registration parameters to compute the distribution of target registration error (TRE) at a desired target location. A new formula for the distribution of TRE, based on the estimated variances, is derived, and it is shown that the computed distribution more accurately follows the real distribution that is generated by the numerical simulations, than the one obtained from the EKF registration algorithm.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 02/2006; 1:497-500.
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ABSTRACT: We propose a novel incremental surface-based registration technique that employs the Unscented Kalman Filter (UKF) to register two different data sets. The method not only reports the variance of the registration parameters but also has significantly more accurate results in comparison to the Iterative Closest Points (ICP) algorithm. Furthermore, it is shown that the proposed incremental registration algorithm is less sensitive to the initial alignment of the data sets than the ICP algorithm. We have validated the method by registering bone surfaces extracted from a set of 3D ultrasound images to the corresponding surface points gathered from the Computed Tomography (CT) data.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2005; 8(Pt 2):197-204.
