Feature-based methods for non-rigid registration frequently encounter the correspondence problem. Regardless of whether points, lines, curves or surface parameterizations are used, feature-based non-rigid matching requires us to automatically solve for correspondences between two sets of features. In addition, there could be many features in either set that have no counterparts in the other. This outlier rejection problem further complicates an already di#cult correspondence problem. We formulate feature-based non-rigid registration as a non-rigid point matching problem. After a careful review of the problem and an in-depth examination of two types of methods previously designed for rigid robust point matching (RPM), we propose a new general framework for non-rigid point matching. We consider it a general framework because it does not depend on any particular form of spatial mapping. We have also developed an algorithm---the TPS-RPM algorithm---with the thin-plate spline (TPS) as the parameterization of the non-rigid spatial mapping and the softassign for the correspondence. The performance of the TPS-RPM algorithm is demonstrated and validated in a series of carefully designed synthetic experiments. In each of these experiments, an empirical comparison with the popular iterated closest point (ICP) algorithm is also provided. Finally, we apply the algorithm to the problem of non-rigid registration of cortical anatomical structures which is required in brain mapping. While these results are somewhat preliminary, they clearly demonstrate the applicability of our approach to real world tasks involving feature-based non-rigid registration.

We have developed a new mutual information-based registration method for matching unlabeled point features. In contrast to earlier mutual information-based registration methods which estimate the mutual information using image intensity information, our approach uses the point feature location information. A novel aspect of our approach is the emergence of correspondence (between the two sets of features) as a natural by-product of joint density estimation. We have applied this algorithm to the problem of geometric alignment of primate autoradiographs. We also present preliminary results on 3D robust matching of sulci derived from anatomical MR. Finally, we present an experimental comparison between the mutual information approach and other recent approaches which explicitly parameterize feature correspondence. Keywords: point feature registration, rigid alignment, mutual information, similarity transformation, spatial mapping, correspondence, joint probability, softassign Received ?...

. Inter-subject non-rigid registration of cortical anatomical structures as seen in MR is a challenging problem. The variability of the sulcal and gyral patterns across patients makes the task of registration especially difficult regardless of whether voxel- or feature-based techniques are used. In this paper, we present an approach to matching sulcal point features interactively extracted by neuroanatomical experts. The robust point matching (RPM) algorithm is used to fine the optimal affine transformations for matching sulcal points. A 3D linearly interpolated non-rigid warping is then generated for the original image volume. We present quantitative and visual comparisons between Talairach, mutual information-based volumetric matching and RPM on five subjects' MR images. 1 Introduction The recent development of brain imaging technologies (PET, MRI, fMRI) has provided rich information on the human brain. A potentially fruitful emerging area of research is human brain mapping [25] whi...

A novel approach has been developed in this thesis for the problem of segmenting volumetric layers, a type of structure often encountered in medical image analysis. This approach is aimed towards the use of structural information to enhance the performance of the segmentation process. While some organs have more consistent global shape and can be characterized using a specific shape model, other anatomical structures possess much more complex shape with possibly high variability which needs a more generic shape constraint. The three-dimensional (3D) nature of anatomical structures necessitates the use of volumetric approaches that exploit complete spatial information and therefore are far superior to the non-optimal and often-biased 2D methods. Our method takes a volumetric approach, and incorporates a generic shape constraint – in particular, a thickness constraint. The resulting coupled surfaces algorithm with a level set implementation not only offers segmentation with the advantages of minimal user interaction, robustness to initialization and computational efficiency, but also facilitates the extraction and measurement of many geometric features of the volumetric layer. The algorithm