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 propose a general tridimensional reconstruction algorithm of range and volumetric images, based on deformable simplex meshes. Simplex meshes are topologically dual of triangulations and have the advantage of permitting smooth deformations in a simple and e cient manner. Our reconstruction algorithm can handle surfaces without any restriction on their shape or topology. The di erent tasks performed during the reconstruction include the segmentation of given objects in the scene, the extrapolation of missing data, and the control of smoothness, density, and geometric quality of the reconstructed meshes. The reconstruction takes place in two stages. First, the initialization stage creates a simplex mesh in the vicinity of the data model either manually or using an automatic procedure. Then, after a few iterations, the mesh topology can be modi ed by creating holes or by increasing its genus. Finally, aniterativere nement algorithm decreases the distance of the mesh from the data while preserving high geometric and topological quality. Several reconstruction examples are provided with quantitative and qualitative results.

Active contour and surface models, also known as deformable models, are powerful image segmentation techniques. Geometric deformable models implemented using level set methods have advantages over parametric models due to their intrinsic behavior, parameterization independence, and ease of implementation. However, a long claimed advantage of geometric deformable models—the ability to automatically handle topology changes—turns out to be a liability in applications where the object to be segmented has a known topology that must be preserved. In this paper, we present a new class of geometric deformable models designed using a novel topology-preserving level set method, which achieves topology preservation by applying the simple point concept from digital topology. These new models maintain the other advantages of standard geometric deformable models including subpixel accuracy and production of nonintersecting curves or surfaces. Moreover, since the topology-preserving constraint is enforced efficiently through local computations, the resulting algorithm incurs only nominal computational overhead over standard geometric deformable models. Several experiments on simulated and real data are provided to demonstrate the performance of this new deformable model algorithm.

An active contour (snake) model is presented, with emphasis on medical imaging applications. There are three main novelties in the proposed model. First, an attribute vector is used to characterize the geometric structure around each point of the snake model; the deformable model then deforms in a way that seeks regions with similar attribute vectors. This is in contrast to most deformable models, which deform to nearby edges without considering geometric structure, and it was motivated by the need to establish point-correspondences that have anatomical meaning. Second, an adaptive-focus statistical model has been suggested which allows the deformation of the active contour in each stage to be influenced primarily by the most reliable matches. Third, a deformation mechanism that is robust to local minima is proposed by evaluating the snake energy function on segments of the snake at a time, instead of individual points. Various experimental results show the effectiveness of th...

A novel, physically motivated deformable model for shape recovery and segmentation is presented. The model, referred to as the charged-particle model (CPM), is inspired by classical electrodynamics and is based on a simulation of charged particles moving in an electrostatic field. The charges are attracted towards the contours of the objects of interest by an electrostatic field, whose sources are computed based on the gradient-magnitude image. The electric field plays the same role as the potential forces in the snake model, while internal interactions are modeled by repulsive Coulomb forces. We demonstrate the flexibility and potential of the model in a wide variety of settings: shape recovery using manual initialization, automatic segmentation, and skeleton computation. We perform a comparative analysis of the proposed model with the active contour model and show that specific problems of the latter are surmounted by our model. The model is easily extendable to 3D and copes well with noisy images.

This paper is an attempt to review the state-of-the-art cortical segmentation techniques in 2-D and 3-D using brain magnetic resonance imaging (MRI), their applications and new challenges

Abstract—This paper proposes a method for detecting object classes that exhibit variable shape structure in heavily cluttered images. The term “variable shape structure ” is used to characterize object classes in which some shape parts can be repeated an arbitrary number of times, some parts can be optional, and some parts can have several alternative appearances. Hidden State Shape Models (HSSMs), a generalization of Hidden Markov Models (HMMs), are introduced to model object classes of variable shape structure using a probabilistic framework. A polynomial inference algorithm automatically determines object location, orientation, scale, and structure by finding the globally optimal registration of model states with the image features, even in the presence of clutter. Experiments with real images demonstrate that the proposed method can localize objects of variable shape structure with high accuracy. For the task of hand shape localization and structure identification, the proposed method is significantly more accurate than previously proposed methods based on chamfer-distance matching. Furthermore, by integrating simple temporal constraints, the proposed method gains speed-ups of more than an order of magnitude and produces highly accurate results in experiments on nonrigid hand motion tracking. Index Terms—Object detection, shape modeling, probabilistic algorithms, dynamic programming. 1

. We present a novel method for fitting a smooth G 1 continuous spline to point sets. It is based on an iterative conjugate gradient optimisation scheme. Unlike traditional tensor product based splines we can fit arbitrary topology surfaces with locally adaptive meshing. For this reason we call the surface "slime". Other attempts at this problem are based on tensor product splines and are therefore not locally adaptive. 1 Introduction To appear at European Conference on Computer Vision (1998), Freiburg, Germany. Range sensing is an area of computer vision that is being successfully applied to a variety of industrial problems. By combining several range images it is possible to build complete detailed surface models of real world objects for applications in VR, graphics and manufacturing [11, 10, 9]. Existing methods produce large datasets consisting of up to a million polygons. There is considerable interest in the use of more efficient representations such as spline surfaces [5, 17...

Communicated by (Silvia Biasotti) We present a novel medial-based, multi-scale approach to shape representation and controlled deformation. We use medial-based profiles for shape representation, which follow the geometry of the structure and describe general, intuitive, and independent shape measures (length, orientation, and thickness). Controlled shape deformations (stretch, bend, and bulge) are obtained either as a result of applying deformation operators at certain locations and scales on the medial profiles, or by varying the weights of the main variation modes obtained from a new hierarchical (multi-scale) and regional (multi-location) principal component analysis of the medial profiles. We demonstrate the ability to produce controlled shape deformations on a medial-based representation of the corpus callosum. We show how this control of shape deformations facilitates the design of a layered framework for image segmentation and present results of segmenting the corpus callosum from 2D mid-sagittal magnetic resonance images of the human ∗ Corresponding author. 1 2 Hamarneh et al brain. Furthermore we show how the medial-based representation facilitates hierarchical, deformation-specific statistical shape analysis of segmented corpora callosa.

Robust, automatic segmentation and analysis of medical images requires powerful and flexible models of anatomical structures. We present a multiscale, medial-based approach to shape representation and controlled deformation in an effort to meet these requirements. We use medial-based profiles for shape representation, which follow the geometry of the structure and describe general, intuitive, and independent shape measures (length, orientation, and thickness). Controlled shape deformations (stretch, bend, and bulge) are obtained either as a result of applying deformation operators at certain locations and scales on the medial profiles, or by varying the weights of the main variation modes obtained from a hierarchical (multiscale) and regional (multi-location) principal component analysis of the medial profiles. We demonstrate the ability to produce controlled shape deformations on a medial-based representation of the corpus callosum. Furthermore, we present results of segmenting the corpus callosum in 2D mid-sagittal MRI slices of the brain. 1