This article surveys deformable models, a promising and vigorously researched computer-assisted medical image analysis technique. Among model-based techniques, deformable models offer a unique and powerful approach to image analysis that combines geometry, physics, and approximation theory. They have proven to be effective in segmenting, matching, and tracking anatomic structures by exploiting (bottom-up) constraints derived from the image data together with (top-down) a priori knowledge about the location, size, and shape of these structures. Deformable models are capable of accommodating the significant variability of biological structures over time and across different individuals. Furthermore, they support highly intuitive interaction mechanisms that, when necessary, allow medical scientists and practitioners to bring their expertise to bear on the model-based image interpretation task. This article reviews the rapidly expanding body of work on the development and application of deformable models to problems of fundamental importance in medical image analysis, includingsegmentation, shape representation, matching, and motion tracking.

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

Abstract. This paper presents a new method for determining the minimal non-rigid deformation between two 3-D surfaces, such as those which describe anatomical structures in 3-D medical images. Although we match surfaces, we represent the deformation as a volumetric transformation. Our method performs a least squares minimization of the distance between the two surfaces of interest. To quickly and accurately compute distances between points on the two surfaces, we use a precomputed distance map represented using an octree spline whose resolution increases near the surface. To quickly and robustly compute the deformation, we use a second octree spline to model the deformation function. The coarsest level of the deformation encodes the global (e.g., affine) transformation between the two surfaces, while finer levels encode smooth local displacements which bring the two surfaces into closer registration. We present experimental results on both synthetic and real 3-D surfaces. 1.

This paper presents a literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject. The relevance of surface registration to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities and which must be reconciled. Surface registration

In this paper we present a new technique for partial surface and volume matching of images in three dimensions. In this

This paper introduces a new free-form surface representation scheme for the purpose of fast and accurate registration and matching. Accurate registration of surfaces is a common task in computer vision. The proposed representation scheme captures the surface curvature information, seen from certain points and produces images, called surface signatures, at these points. Matching signatures of di#erent surfaces enables the recovery of the transformation parameters between these surfaces. We propose to use template matching to compare the signature images. To enable partial matching, another criterion, the overlap ratio, is used. This representation scheme can beusedasa global representation of the surface as well as a local one and performs near real-time registration. We show that the signaturerepresentation can beusedtomatch objects in 3-D scenes in the presence of clutter and occlusion. Applications presented include free-form object matching, multimodal medical volumes registration and dental teeth reconstruction from intra-oral images. I

Abstract. Correlation is a very effective way to align intensity images. We extend the correlation technique to point set registration using a method we call kernel correlation. Kernel correlation is an affinity measure, and it is also a function of the point set entropy. We define the point set registration problem as finding the maximum kernel correlation configuration of the the two point sets to be registered. The new registration method has intuitive interpretations, simple to implement algorithm and easy to prove convergence property. Our method shows favorable performance when compared with the iterative closest point (ICP) and EM-ICP methods. 1

Conformal radiotherapy treatments need accurate patient positioning in order to spare normal tissues. Patient pose can be evaluated by registering portal images (PI) with Digitally Reconstructed Radiographs (DRR). Several methods involve segmentation which is known to be a dicult task for noisy PI. In this paper, we study another approach by using a fully 3D intensity-based registration method, without segmentation. Our approach uses the correlation ratio as similarity measure and replace DRR generation with a treatment on pre-computed DRR. A specic geometrical transformation is applied to approximate a given projection by the composition of out-of-plane rotations and inplane transformation. Some preliminary experiments on both simulated and real portal images, lead to good results (RMS error lower than 2 mm). 1 Medical context

Abstract—In this paper, we tackle the problem of geometric and photometric modeling of large intricately shaped objects. Typical target objects we consider are cultural heritage objects. When constructing models of such objects, we are faced with several important issues that have not been addressed in the past—issues that mainly arise due to the large amount of data that has to be handled. We propose two novel approaches to efficiently handle such large amounts of data: A highly adaptive algorithm for merging range images and an adaptive nearest-neighbor search to be used with the algorithm. We construct an integrated mesh model of the target object in adaptive resolution, taking into account the geometric and/or photometric attributes associated with the range images. We use surface curvature for the geometric attributes and (laser) reflectance values for the photometric attributes. This adaptive merging framework leads to a significant reduction in the necessary amount of computational resources. Furthermore, the resulting adaptive mesh models can be of great use for applications such as texture mapping, as we will briefly demonstrate. Additionally, we propose an additional test for the k-d tree nearest-neighbor search algorithm. Our approach successfully omits back-tracking, which is controlled adaptively depending on the distance to the nearest neighbor. Since the main consumption of computational cost lies in the nearest-neighbor search, the proposed algorithm leads to a significant speed-up of the whole merging process. In this paper, we present the theories and algorithms of our approaches with pseudo code and apply them to several real objects, including large-scale cultural assets. Index Terms—Adaptive integration of range images, laser reflectance strength, nearest-neighbor search. 1

1 In this paper we present a method for 3-D reconstruction of human bodies with application in CAD systems for garment design. The reconstruction scheme uses image information from several arbitrary views and deformable superquadrics as the models of the body parts. Two visual cues are used: occluding contours and stereo (possibly aided by projected patterns) . Our preliminary experiments show that the reconstruction is more complete than in purely stereo or structured light based methods and more precise than the reconstruction from occluding contours only. From the reconstructed human body, the body measurements can be taken automatically, and used in garment design. We give an example of draping of virtual garment over the photo-realistic 3D model of the imaged human. One can easily envision the use of the described algorithms in the development of custom-fit garment retail software over the Internet, which would include the possibility of trying the garment on in virtual reality. ...