The purpose of this chapter is to present a survey of recent publications concerning medical image registration techniques. These publications will be classified according to a model based on nine salient criteria, the main dichotomy of which is extrinsic versus intrinsic methods The statistics of the classification show definite trends in the evolving registration techniques, which will be discussed. At this moment, the bulk of interesting intrinsic methods is either based on segmented points or surfaces, or on techniques endeavoring to use the full information content of the images involved. Keywords: registration, matching Received May 25, 1997

In this paper, we extend a previously reported intensity-based nonrigid registration algorithm by using a novel regularization term to constrain the deformation. Global motion is modeled by a rigid transformation while local motion is described by a free-form deformation based on B-splines. An information theoretic measure, normalized mutual information, is used as an intensity-based image similarity measure. Registration is performed by searching for the deformation that minimizes a cost function consisting of a weighted combination of the image similarity measure and a regularization term. The novel regularization term is a local volume-preservation (incompressibility) constraint, which is motivated by the assumption that soft tissue is incompressible for small deformations and short time periods. The incompressibility constraint is implemented by penalizing deviations of the Jacobian determinant of the deformation from unity. We apply the nonrigid registration algorithm with and without the incompressibility constraint to precontrast and postcontrast magnetic resonance (MR) breast images from 17 patients. Without using a constraint, the volume of contrast-enhancing lesions decreases by 1%--78% (mean 26%). Image improvement (motion artifact reduction) obtained using the new constraint is compared with that obtained using a smoothness constraint based on the bending energy of the coordinate grid by blinded visual assessment of maximum intensity projections of subtraction images. For both constraints, volume preservation improves, and motion artifact correction worsens, as the weight of the constraint penalty term increases. For a given volume change of the contrast-enhancing lesions (2% of the original volume), the incompressibility constraint reduces motion artifacts ...

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This manuscript tackles the registration of 2D biological images (histological sections or autoradiographs) to 2D images from the same or di#erent modalities (e.g., histology or MRI). The process of acquiring these images typically induces composite transformations that can be modeled as a number of rigid or a#ne local transformations embedded in an elastic one. We propose a registration approach closely derived from this model. Given a pair of input images, we first compute a dense similarity field between them with a block matching algorithm. A hierarchical clustering algorithm then automatically partitions this field into a number of classes from which we extract independent pairs of sub-images. Finally, the pairs of sub-images are, independently, a#nely registered and a hybrid a#ne/non-linear interpolation scheme is used to compose the output registered image. We investigate the behavior of our approach under a variety of conditions, and discuss examples using real biomedical images, including MRI, histology and cryosection data.

We introduce radial basis functions with compact support for elastic registration of medical images. With these basis functions the influence of a landmark on the registration result is limited to a circle in 2D and, respectively, to a sphere in 3D. Therefore, the registration can be locally constrained which especially allows to deal with rather local changes in medical images due to, e.g., tumor resection. An important property of the used RBFs is that they are positive definite. Thus, the solvability of the resulting system of equations is always guaranteed. We demonstrate our approach for synthetic as well as for 2D and 3D tomographic images. 1. Introduction Registration is an important technique in medical image analysis. Rigid and affine registration methods can only cope with global differences, for example, translation, rotation, and scaling. In many cases, however, elastic or nonrigid methods are required to cope with local differences between the images. Such differences ar...

Regularized nonrigid medical image registration algorithms usually estimate the deformation by minimizing a cost function, consisting of a similarity measure and a penalty term that discourages “unreasonable ” deformations. Conventional regularization methods enforce homogeneous smoothness properties of the deformation field; less work has been done to incorporate tissue-type-specific elasticity information. Yet ignoring the elasticity differences between tissue types can result in non-physical results, such as bone warping. Bone structures should move rigidly (locally), unlike the more elastic deformation of soft issues. Existing solutions for this problem either treat different regions of an image independently, which requires precise segmentation and incurs boundary issues; or use an empirical spatial varying “filter ” to “correct” the deformation field, which requires the knowledge of a stiffness map and departs from the cost-function formulation. We propose a new approach to incorporate tissue rigidity information into the nonrigid registration problem, by developing a space variant regularization function that encourages the local Jacobian of the deformation to be a nearly orthogonal matrix in rigid image regions, while allowing more elastic deformations elsewhere. For the case of X-ray CT data, we use a simple monotonic increasing function of the CT numbers (in HU) as a “rigidity index ” since bones typically have the highest CT numbers. Unlike segmentation-based methods, this approach is flexible enough to account for partial volume effects. Results using a B-spline deformation parameterization illustrate that the proposed approach improves registration accuracy in inhale-exhale CT scans with minimal computational penalty. Keywords: X-ray computed tomography (CT), regularization, homomorphism, orthogonal matrix, Frobenius norm 1.

Abstract. This paper describes an algorithm for unbiased construction of white matter (WM) atlases using full information available to diffusion tensor (DT) images. The key component of the proposed algorithm is a novel DT image registration method that leverages metrics comparing tensors as a whole and optimizes tensor orientation explicitly. The problem of unbiased atlas construction is formulated using the approach proposed by Joshi et al., i.e., the unbiased WM atlas is determined by finding the mappings that best match the atlas to the images in the population and have the least amount of deformation. We show how the proposed registration algorithm can be adapted to approximately find the optimal atlas. The utility of the proposed approach is demonstrated by constructing a WM atlas of 13 subjects. The presented DT registration method is also compared to the approach of matching DT images by aligning their fractional anisotropy images using large-deformation image registration methods. Our results suggest that using full tensor information can better align the orientations of WM fiber bundles. 1

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put of the detector plane of (c) is shown in (e). The entire surface is smoother than (d). The uncorrupted corner and the blurred feature give a less pronounced peak; the position of the corrupted corner cannot be detected with confidence and several likely locations are indicated by the smooth hill. Thus, detection and placement can be improved by using sharp feature representations. The aim of this chapter is to develop feature sets with sharp contours. Three amendments to the previously proposed architecture are proposed: the use of spatial competition during training is outlined in x6.2, the selection of a subset of features from a larger set is suggested in x6.3, and the application of threshold-like, feature post-processing is discussed in x6.4. First a description of the three methods is given which is followed by an experimental investigation in x6.5. The new feature types of the three methods are given in

. A new algorithm is proposed to incorporate orientation attributes at landmarks in an elastic medical image registration scheme. The approach is based on a minimizing functional and can cope with images of arbitrary dimensions. Constraints due to orientations are incorporated through scalar products between the transformed orientations in the source image and vectors which are perpendicular to the orientations in the target image. For synthetic data sets we show that the structure of a rigid object in an otherwise elastically deformed image can be preserved. The application of our approach to tomographic data sets shows that improvements w.r.t. accuracy can be achieved provided a sufficient number of landmarks is well-distributed over the image. 1 Introduction Landmark-based elastic registration relies on the information associated with landmark points. To improve the results of landmark-based approaches while maintaining their efficiency, work in the past has been done on including ...