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

A new information-theoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative pose until the mutual information between images is maximized. In our derivation of the registration procedure, few assumptions are made about the nature of the imaging process. As a result the algorithms are quite general and can foreseeably be used with a wide variety of imaging devices. This approach works directly with raw images; no preprocessing or feature detection is required. As opposed to feature-based techniques, all of the information in the scan is used to evaluate the registration. This technique is however more flexible and robust than other intensity based techniques like correlation. Additionally, it has an efficient implementation that is based on stochastic approximation. Experiments are presented that demonstrate the approach registering magnetic resonance (MR) images with comput...

Abstract not found

There is a need for frameless guidance systems to help surgeons plan the exact location for incisions, to define the margins of tumors and to precisely identify locations of neighboring critical structures. We have developed an automatic technique for registering clinical data, such as segmented MRI or CT reconstructions, with any view of the patient on the operating table, using a series of registration algorithms, which we demonstrate on the specific example of neurosurgery. The method enables a visual mix of live video of the patient with the segmented 3D MRI or CT model, supporting enhanced reality techniques for planning and guiding neurosurgical procedures, and to interactively view extracranial or intracranial structures non-intrusively. Extensions of the method include image guided biopsies, focused therapeutic procedures and clinical studies involving change detection over time sequences of images. 1 Artificial Intelligence Laboratory, Massachusetts Institute of Tech...

. This paper examines statistical approaches to model-based object recognition. Evidence is presented indicating that, in some domains, normal (Gaussian) distributions are more accurate than uniform distributions for modeling feature fluctuations. This motivates the development of new maximum-likelihood and MAP recognition formulations which are based on normal feature models. These formulations lead to an expression for the posterior probability of the pose and correspondences given an image. Several avenues are explored for specifying a recognition hypothesis. In the first approach, correspondences are included as a part of the hypotheses. Search for solutions may be ordered as a combinatorial search in correspondence space, or as a search over pose space, where the same criterion can equivalently be viewed as a robust variant of chamfer matching. In the second approach, correspondences are not viewed as being a part of the hypotheses. This leads to a criterion that is a smooth funct...

This paper describes a new method for determining correspondence between points on pairs of surfaces based on shape using a combination of geodesic distance and surface curvature. An initial sparse set of corresponding points are generated using a shapebased matching procedure. Geodesic interpolation is employed in order to capture the complex surface. In addition, surface correspondence and triangulation are computed simultaneously in a hierarchical way. Results applied to human cerebral cortical surfaces are shown to evaluate the approach. 1 Introduction Determining the correspondence of 3D points between pairs of surfaces has many important applications such as for comparing shape between deformable objects, non-rigid registration, developing probabilistic models and atlases, etc. While shape provides the basis for such a correspondence, this problem remains a di#cult one due to ambiguity when the surfaces are complex and variable, such as with the human cerebral cortex. In additi...

We present a novel method for the geometric alignment of autoradiographs of the brain. The method is based on finding the spatial mapping and the one-to-one correspondences (or homologies) between point features extracted from the images and rejecting non-homologies as outliers. In this way, we attempt to account for the local natural and artifactual differences between the autoradiograph slices. We have executed the resulting automated algorithm on a set of left prefrontal cortex autoradiograph slices, specifically demonstrated its ability to perform point outlier rejection, validated it using synthetically generated spatial mappings and provided a visual comparison against the well known iterated closest point (ICP) algorithm. Visualization of a stack of aligned left prefrontal cortex autoradiograph slices is also provided.

The goal of intra-surgical registration is to establish a common reference frame between pre-surgical and intra-surgical 3-D data sets that correspond to the same anatomy. This paper presents two novel techniques which have application to this problem: high-speed pose tracking, and intra-surgical data selection. In the first part of this paper, we describe an approach for tracking the pose of arbitrarily-shaped rigid objects at rates up to 10Hz. Static accuracies on the order of 1mm in translation and 1 degree in rotation have been achieved. We have demonstrated the technique on a human face using a high-speed VLSI range sensor; however, the technique is independent of the sensor used or the anatomy tracked. In the second part of this paper, we describe a general purpose approach for selecting near-optimal, intrasurgical registration data. Due to high costs associated with the acquisition of intra-surgical data, it is desirable to minimize the amount of data acquired, while ensuring that registration accuracy requirements are met. We synthesize near-optimal intra-surgical data sets, based upon an analysis of differential surface properties of pre-surgical data. We demonstrate, using data from a human femur, that discrete point data sets selected using our method provide superior pose refinement accuracy to those selected by

Introduction The ever expanding gamut of medical imaging techniques provides the clinician an increasingly multifaceted view of brain function and anatomy. The information provided by the various imaging modalities is often complementary (i.e. provides separate but useful information) and synergistic (i.e. the combination of information provides useful extra information). For example, X-ray computed tomography (CT) and magnetic resonance (MR) imaging exquisitely demonstrate brain anatomy but provide little functional information. Positron emission tomography (PET) and single photon emission computed tomography (SPECT) scans display aspects of brain function and allow metabolic measurements but poorly delineate anatomy. Furthermore, CT and MR images describe complementary morphologic features. For example, bone and calcifications are best seen on CT images, while soft-tissue structures are better differentiated by MR imaging. Clinical diagnosis and therapy planning and evaluatio

Abstract — The registration of retinal images is required to facilitate the study of the optic nerve head and the retina. The method we propose combines the use of mutual information as the similarity measure and simulated annealing as the search technique. It is robust toward large transformations between the images and significant changes in light intensity. By using a pyramid sampling approach combined with simulated reannealing we find that registration can be achieved to predetermined precision, subject to choice of interpolation and the constraint of time. The algorithm was tested on 49 pairs of stereo images and 48 pairs of temporal images with success. Index Terms—Hierarchical systems, image registration, information theory, simulated annealing, visual system.