Abstract — In this paper we present a new approach for the nonrigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modeled by an affine transformation while the local breast motion is described by a free-form deformation (FFD) based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as a result of the contrast enhancement. Registration is achieved by minimizing a cost function, which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of three-dimensional (3-D) breast MRI in volunteers and patients. In particular, we have compared the results of the proposed nonrigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the nonrigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms. I.

This paper presents a technique for adapting existing motion of a human-like character to have the desired features that are specified by a set of constraints. This problem can be typically formulated as a spacetime constraint problem. Our approach combines a hierarchical curve fitting technique with a new inverse kinematics solver. Using the kinematics solver, we can adjust the configuration of an articulated figure to meet the constraints in each frame. Through the fitting technique, the motion displacement of every joint at each constrained frame is interpolated and thus smoothly propagated to frames. We are able to adaptively add motion details to satisfy the constraints within a specified tolerance by adopting a multilevel B-spline representation which also provides a speedup for the interpolation. The performance of our system is further enhanced by the new inverse kinematics solver. We present a closed-form solution to compute the joint angles of a limb linkage. This analytical m...

Finite element solvers are a basic component of simulation applications; they are common in computer graphics, engineering, and medical simulations. Although adaptive solvers can be of great value in reducing the often high computational cost of simulations they are not employed broadly. Indeed, building adaptive solvers can be a daunting task especially for 3D finite elements. In this paper we are introducing a new approach to produce conforming, hierarchical, adaptive refinement methods (CHARMS). The basic principle of our approach is to refine basis functions, not elements. This removes a number of implementation headaches associated with other approaches and is a general technique independent of domain dimension (here 2D and 3D), element type (e.g., triangle, quad, tetrahedron, hexahedron), and basis function order (piecewise linear, higher order B-splines, Loop subdivision, etc.). The (un-)refinement algorithms are simple and require little in terms of data structure support. We demonstrate the versatility of our new approach through 2D and 3D examples, including medical applications and thin-shell animations.

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 ...

Barycentric coordinates for triangles are commonly used in computer graphics, geometric modelling, and other computational sciences for various purposes, because they provide a convenient way to linearly interpolate data that is given at the corners of a triangle. The concept of barycentric coordinates can also be extended in several ways to convex polygons with more than three vertices, but most of these constructions break down when used in the non-convex setting. One choice that is not limited to convex configurations are the mean value coordinates and we show that they are well-defined for arbitrary planar polygons without self-intersections. Besides many other important properties, these coordinate functions are smooth and allow an efficient and robust implementation. They are particularly useful for interpolating data that is given at the vertices of the polygons and we present several examples of their application to common problems in computer graphics and geometric modelling.

In this paper, we demonstrate a technique for modeling liver motion during the respiratory cycle using intensitybased free-form deformation registration of gated MR images. We acquired 3-D MR image sets (multislice 2-D) of the abdomen of four volunteers at end-inhalation, end-exhalation, and eight time points in between using respiratory gating. We computed the deformation field between the images using intensity-based rigid and non-rigid registration algorithms. The non-rigid transformation is a free-form deformation with B-spline interpolation between uniformlyspaced control points. The transformations between inhalation and exhalation were visually inspected. Much of the liver motion is cranial-caudal translation, and thus the rigid transformation captures much of the motion. However, there is still substantial residual deformation of up to 2 cm. The free-form deformation produces a motion field that appears on visual inspection to be accurate. This is true for the liver surface, internal liver structures such as the vascular tree, and the external skin surface. We conclude that abdominal organ motion due to respiration can be satisfactorily modeled using an intensity-based non-rigid 4-D image registration approach. This allows for an easier and potentially more accurate and patient-specific deformation field computation than physics-based models using assumed tissue properties and acting forces.

this article, we present a general framework for polymorphing by extending the traditional image morphing paradigm that applies to twoimages. We formulate each input image as a vertex of an (n - 1)-dimensional simplex, where n equals the number of input images. Note that an (n - 1)-dimensional simplex is a convex polyhedron having n vertices in (n - 1)-dimensional space, such as a triangle in 2D or a tetrahedron in 3D. An arbitrary inbetween (morphed) image can be specified by a point in the simplex. The barycentric coordinates of that point determine the weights used to blend the input images into the in-between image. When considering only two images, the simplex degenerates into a line. Points along the line correspond to in-between images in a morph sequence. This case is identical to conventional image morphing. When considering more than two images, a path lying anywhere in the simplex constitutes the inbetween images in a morph sequence

Animating 3D faces to achieve compelling realism is a challenging task in the entertainment industry. Previously proposed face transfer approaches generally require a high-quality animated source face in order to transfer its motion to new 3D faces. In this work, we present a semi-automatic technique to directly animate popularized 3D blendshape face models by mapping facial motion capture data spaces to 3D blendshape face spaces. After sparse markers on the face of a human subject are captured by motion capture systems while a video camera is simultaneously used to record his/her front face, then we carefully select a few motion capture frames and accompanying video frames as reference mocap-video pairs. Users manually tune blendshape weights to perceptually match the animated blendshape face models with reference facial images (the reference mocap-video pairs) in order to create reference mocapweight pairs. Finally, the Radial Basis Function (RBF) regression technique is used to map any new facial motion capture frame to blendshape weights based on the reference mocap-weight pairs. Our results demonstrate that this technique is efficient to animate blendshape face models, while offering its generality and flexiblity.

When human raters are presented with a collection of shapes and asked to rank them according to their aesthetic appeal, the results often indicate that there is a statistical consensus among the raters. Yet it might be difficult to define a succinct set of rules that capture the aesthetic preferences of the raters. In this work, we explore a data-driven approach to aesthetic enhancement of such shapes. Specifically, we focus on the challenging problem of enhancing the aesthetic appeal (or the attractiveness) of human faces in frontal photographs (portraits), while maintaining close similarity with the original. The key component in our approach is an automatic facial attractiveness engine trained on datasets of faces with accompanying facial attractiveness ratings collected from groups of human raters. Given a new face, we extract a set of distances between a variety of facial feature locations, which define a point in a high-dimensional “face space”. We then search the face space for a nearby point with a higher predicted attractiveness rating. Once such a point is found, the corresponding facial distances are embedded in the plane and serve as a target to define a 2D warp field which maps the original facial features to their adjusted locations. The effectiveness of our technique was experimentally validated by independent rating experiments, which indicate that it is indeed capable of increasing the facial attractiveness of most portraits that we have experimented with. Keywords: warping 1

We describe a method to generate an average atlas from segmented 3-D images of a population of subjects. Using repeated application of an intensity-based non-rigid registration algorithm based on third-order 3-D B-splines, a sequence of average label images is created. Averaging of the non-numerical label data employs a generalization of the mode of sets of corresponding voxels, parameterized by a threshold value specifying the required level of classification confidence. The number of voxels that cannot be assigned a unique average value provides a criterion for the convergence of the iteration. For improved accuracy, efficiency, and robustness of the non-rigid registration, deformations computed during one iteration are propagated to the next iteration as initial transformation estimates. The usefulness of our method is demonstrated by applying it to generate an average atlas from segmented 3-D confocal microscopy images of 20 bee brains. We validate that the deformations found by our algorithm are meaningful by deforming the original gray-level images according to the transformations computed for the label fields.