We perform the unbiased affine registration of 5 multimodality images of anatomy, CT, MR PD, T1 and T2 from Visible Human Male Data and the unbiased nonrigid registration of three MR 3D images of the brain with the normalized metric and high-dimensional histogramming . Our results demonstrate the efficacy of the metrics and high-dimensional histogramming in affine and nonrigid multimodality image registration.

Image registration is the process of systematically placing separate images in a common frame of reference so that the information they contain can be optimally integrated or compared. This is becoming the central tool for image analysis, understanding, and visualization in both medical and scientific applications. Medical Image Registration provid

The 6th International Conference on Medical Imaging and Computer-Assisted Intervention,MICCAI2003,washeldinMontr ́ eal,Qu ́ ebec,CanadaattheF- rmont Queen Elizabeth Hotel during November 15–18, 2003. This was the ?rst time the conference had been held in Canada. The proposal to host MICCAI 2003 originated from discussions within the Ontario Consortium for Ima- guided Therapy and Surgery, a multi-institutional research consortium that was supported by the Government of Ontario through the Ontario Ministry of E- erprise, Opportunity and Innovation. The objective of the conference was to o?er clinicians and scientists a - rum within which to exchange ideas in this exciting and rapidly growing ?eld. MICCAI 2003 encompassed the state of the art in computer-assisted interv- tions, medical robotics, and medical-image processing, attracting experts from numerous multidisciplinary professions that included clinicians and surgeons, computer scientists, medical physicists, and mechanical, electrical and biome- cal engineers. The quality and quantity of submitted papers were most impressive. For MICCAI 2003 we received a record 499 full submissions and 100 short c- munications. All full submissions, of 8 pages each, were reviewed by up to 5 reviewers, and the 2-page contributions were assessed by a small subcomm- tee of the Scienti?c Review Committee. All reviews were then considered by the MICCAI 2003 Program Committee, resulting in the acceptance of 206 full papers and 25 short communications. The normal mode of presentation at MICCAI 2003 was as a poster; in addition, 49 papers were chosen for oral presentation.

"Medical image registration has received considerable attention in medical imaging and computer vision, because of the large variety of ways in which it can impact patient care. Over the years, many algorithms have been proposed for medical image registration. Medical image registration uses techniques to create images of parts of the human body for clinical purposes. This thesis focuses on one small subset of registration algorithms: using machine learning techniques to train the similarity measure for use in medical image registration. This thesis is organized in the following manner. In Chapter 1 we introduce the idea of image registration, describe some some applications in medical imaging, and mathematically formulate the three main components of any registration problem: geometric transformation, similarity measure and optimization procedure. Finally we describe how the ideas in this thesis fit into the field of medical image registration, and we describe some related work. In Chapter 2 we introduce the concept of machine learning and we provide examples to illustrate machine learning algorithms. We then describe the knn-nearest neighbors algorithm and the relationship between Euclidean and Mahalanobis distance. Next we introduce distance metric learning and present two approaches for learning the Mahalanobis distance. Finally we provide a description and visual comparison of two algorithms for distance metric learning. In Chapter 3 we describe how distance metric learning can be applied to the problem of medical image registration. Our goal is to learn the optimal similarity measure given a training dataset of correctly registered images. To assess the performance of the two distance metric learning algorithms we test them using images from a series of patients. Moreover we illustrate the sensitivity of one of the learning algorithms by examining the variability of the resulting target registration errors. Finally we present our experimental results of registering CT and MR images. Finally in Chapter 4 we suggest some ideas for future work in order to improve our registration results and to speed up the algorithms."--Abstract.

A hands-on guide to image registration theory and methods—with examples of a wide range of real-world applications Theory and Applications of Image Registration offers comprehensive coverage of feature-based image registration methods. It provides in-depth exploration of an array of fundamental issues, including image orientation detection, similarity measures, feature extraction methods, and elastic transformation functions. Also covered are robust parameter estimation, validation methods, multi-temporal and multi-modality image registration, methods for determining the orientation of an image, methods for identifying locally unique neighborhoods in an image, methods for detecting lines in an image, methods for finding corresponding points and corresponding lines in images, registration of video images to create panoramas, and much more. Theory and Applications of Image Registration provides readers with a practical guide to the theory and underpinning principles. Throughout the book numerous real-world examples are given, illustrating how image registration can be applied to problems in various fields, including biomedicine, remote sensing, and computer vision. Also provided are software routines to help readers develop their image registration skills. Many of the algorithms described in the book have been implemented, and the software packages are made available to the readers of the book on a companion website. In addition, the book: Explores the fundamentals of image registration and provides a comprehensive look at its multi-disciplinary applications Reviews real-world applications of image registration in the fields of biomedical imaging, remote sensing, computer vision, and more Discusses methods in the registration of long videos in target tracking and 3-D reconstruction Addresses key research topics and explores potential solutions to a number of open problems in image registration Includes a companion website featuring fully implemented algorithms and image registration software for hands-on learning Theory and Applications of Image Registration is a valuable resource for researchers and professionals working in industry and government agencies where image registration techniques are routinely employed. It is also an excellent supplementary text for graduate students in computer science, electrical engineering, software engineering, and medical physics.