A heuristic method has been developed for registering two sets of 3-D curves obtained by using an edge-based stereo system, or two dense 3-D maps obtained by using a correlation-based stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually approximately known. From this initial estimate, our algorithm computes observer motion with very good precision, which is required for environment modeling (e.g., building a Digital Elevation Map). Objects are represented by a set of 3-D points, which are considered as the samples of a surface. No constraint is imposed on the form of the objects. The proposed algorithm is based on iteratively matching points in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subset-subset matching. A least-squares technique is used to estimate 3-D motion from the point correspondences, which reduces the average distance between points in the two sets. Both synthetic and real data have been used to test the algorithm, and the results show that it is efficient and robust, and yields an accurate motion estimate.

Abstract—Motivated by the problem of retinal image registration, this paper introduces and analyzes a new registration algorithm called Dual-Bootstrap Iterative Closest Point (Dual-Bootstrap ICP). The approach is to start from one or more initial, low-order estimates that are only accurate in small image regions, called bootstrap regions. In each bootstrap region, the algorithm iteratively: 1) refines the transformation estimate using constraints only from within the bootstrap region; 2) expands the bootstrap region; and 3) tests to see if a higher order transformation model can be used, stopping when the region expands to cover the overlap between images. Steps 1): and 3), the bootstrap steps, are governed by the covariance matrix of the estimated transformation. Estimation refinement [Step 2)] uses a novel robust version of the ICP algorithm. In registering retinal image pairs, Dual-Bootstrap ICP is initialized by automatically matching individual vascular landmarks, and it aligns images based on detected blood vessel centerlines. The resulting quadratic transformations are accurate to less than a pixel. On tests involving approximately 6000 image pairs, it successfully registered 99.5 % of the pairs containing at least one common landmark, and 100 % of the pairs containing at least one common landmark and at least 35 % image overlap. Index Terms—Iterative closest point, medical imaging, registration, retinal imaging, robust estimation.

The Iterative Closest Point (ICP) algorithm is a widely used method for aligning three-dimensional point sets. The quality of alignment obtained by this algorithm depends heavily on choosing good pairs of corresponding points in the two datasets. If too many points are chosen from featureless regions of the data, the algorithm converges slowly, finds the wrong pose, or even diverges, especially in the presence of noise or miscalibration in the input data. In this paper, we describe a method for detecting uncertainty in pose, and we propose a point selection strategy for ICP that minimizes this uncertainty by choosing samples that constrain potentially unstable transformations.

: Some medical interventions require knowing the correspondence between an MRI/CT image and the actual position of the patient. Examples occur in neurosurgery and radiotherapy, but also in video surgery (laparoscopy). We present in this paper three new techniques for performing this task without artificial markers. To do this, we find the 3D2D projective transformation (composition of a rigid displacement and a perspective projection) which maps a 3D object onto a 2D image of this object. Depending on the object model (curve or surface), and on the 2D image acquisition system (X-Ray, video), the techniques are different but the framework is common: ffl We first find an estimate of the transformation using bitangent lines or bitangent planes. These are first order semi-differential invariants [GMPO92]. ffl Then, introducing the normal or tangent, we define a distance between the 3D object and the 2D image, and we minimize it using extensions of the Iterative Closest Point algorithm (...

The goal of intra-surgical registration is to establish a common reference frame between pre-surgical and intra-surgical 3-D data sets that correspond to the same anatomy. This paper presents two novel techniques which have application to this problem: high-speed pose tracking, and intra-surgical data selection. In the first part of this paper, we describe an approach for tracking the pose of arbitrarily-shaped rigid objects at rates up to 10Hz. Static accuracies on the order of 1mm in translation and 1 degree in rotation have been achieved. We have demonstrated the technique on a human face using a high-speed VLSI range sensor; however, the technique is independent of the sensor used or the anatomy tracked. In the second part of this paper, we describe a general purpose approach for selecting near-optimal, intrasurgical registration data. Due to high costs associated with the acquisition of intra-surgical data, it is desirable to minimize the amount of data acquired, while ensuring that registration accuracy requirements are met. We synthesize near-optimal intra-surgical data sets, based upon an analysis of differential surface properties of pre-surgical data. We demonstrate, using data from a human femur, that discrete point data sets selected using our method provide superior pose refinement accuracy to those selected by

We present in this paper a new registration and gain correction algorithm for 3D medical images. It is intensity based. The basic idea is to represent images by 4D points (xj;yj;zj;ij) and to de ne a global energy function based on this representation. For minimisation, we propose a technique which does not require computing the derivatives of this criterion with respect to the parameters. It can be understood as an extension of the Iterative Closest Point algorithm [5, 56] or as an application of the formalism proposed in [13]. Two parameters enable us to develop a coarse-to- ne strategy both for resolution and for deformation. Our technique presents the advantage of minimising a well-de ned global criterion, to deal with various classes of transformations (for example rigid, a ne, volume spline and radial basis functions), to be simple to implement, and to be e cient in practice. Results on real brain and heart 3D images are presented to demonstrate the validity of our approach. We also explain how one can compute basic statistics on the deformation parameters to constrain the set of possible deformations by learning and to discriminate between di erent groups. 2 1

Despite their great accuracy, coordinate measuring machines (CMM)...

This paper describes a new model to range data registration algorithm, specifically designed for accuracy, speed, and robustness. Like many recent registration techniques, our Robust-Closest-Patch algorithm (RCP) iteratively matches model patches to data surfaces based on the current pose and then re-estimates pose based on these matches. RCP has several novel features: 1) on-line registration is driven by low curvature patches computed from the model off-line; 2) an approximate normal distance between a patch and a surface is used, avoiding the need to estimate local surface normal and curvature from noisy data; 3) pose is solved exactly by a linear system in six parameters, using a symmetric formulation of the rotation constraint; 4) robustness is ensured using an M-estimator that estimates both the rigid pose parameters and the error standard deviation. Results are shown using models and range data from turbine blade inspection.

. Shape-based registration is a process for estimating the transformation between two shape representations of an object. It is used in many image-guided surgical systems to establish a transformation between pre- and intra-operative coordinate systems. This paper describes several tools which are useful for improving the accuracy resulting from shape-based registration: constraint analysis, constraint synthesis, and online accuracy estimation. Constraint analysis provides a scalar measure of sensitivity which is well correlated with registration accuracy. This measure can be used as a criterion function by constraint synthesis, an optimization process which generates configurations of registration data which maximize expected accuracy. Online accuracy estimation uses a conventional rootmean -squared error measure coupled with constraint analysis to estimate an upper bound on true registration error. This paper demonstrates that registration accuracy can be significantly improved via a...

Abstract--We present an automatic algorithm of digitization on a CNC coordinate measuring machine for reverse engineering of an unknown free-formed surface. This algorithm supervises the movement of a contact type ball-tip probe in an autonomous manner in that the surface is discretely sampled to be fitted by a polyhedron surface model of triangular plane patches. Vertex points of the polyhedron are measured so that the surface is approximated with a minimum number of point data satisfying a specified geometric tolerance. An effective compensation algorithm of ball-tip radius is also proposed to obtain an actual surface profile from the digitized data. © 1997 Published by Elsevier Science Ltd 1.