This text provides an introduction to image registration with particular emphasis on numerical methods in medical imaging. Designed for researchers in industry and academia, it should also be a suitable study guide for graduate mathematicians, computer scientists and medical physicists.

This book contains eleven original and survey scientific research articles arose from presentations given by invited speakers at International Workshop on Image Processing and Inverse Problems, held in Beijing Computational Science Research Center, Beijing, China, April 21–24, 2018. The book was dedicated to Professor Raymond Chan on the occasion of his 60th birthday. The contents of the book cover topics including image reconstruction, image segmentation, image registration, inverse problems and so on. Deep learning, PDE, statistical theory based research methods and techniques were discussed. The state-of-the-art developments on mathematical analysis, advanced modeling, efficient algorithm and applications were presented. The collected papers in this book also give new research trends in deep learning and optimization for imaging science. It should be a good reference for researchers working on related problems, as well as for researchers working on computer vision and visualization, inverse problems, image processing and medical imaging.

Here is, for the first time, a book that clearly explains and applies new level set methods to problems and applications in computer vision, graphics, and imaging. It is an essential compilation of survey chapters from the leading researchers in the field. The applications of the methods are emphasized.

The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful.

This book provides a summary of current research in the application of image registration to satellite imagery. Presenting algorithms for creating mosaics and tracking changes on the planet's surface over time, it is an indispensable resource for researchers and advanced students in Earth and space science, and image processing.

Variational Methods in Image Processing presents the principles, techniques, and applications of variational image processing. The text focuses on variational models, their corresponding Euler-Lagrange equations, and numerical implementations for image processing. It balances traditional computational models with more modern techniques that solve t

Based on the author's lecture notes and research, this well-illustrated and comprehensive text is one of the first to provide an introduction to image registration with particular emphasis on numerical methods in medical imaging. Ideal for researchers in industry and academia, it is also a suitable study guide for graduate mathematicians, computer scientists, engineers, medical physicists, and radiologists. Image registration is utilised whenever information obtained from different viewpoints needs to be combined or compared and unwanted distortion needs to be eliminated. For example, CCTV images, ultrasound images, brain scan images, fingerprint and retinal scanning. Modersitzki's book provides a systematic introduction to the theoretical, practical, and numerical aspects of image registration, with special emphasis on medical applications. Various techniques are described, discussed and compared using numerous illustrations. The text starts with an introduction to the mathematical principles and the motivating example of the Human Neuroscanning Project whose aim is to build an atlas of the human brain through reconstructing essential information out of deformed images of sections of a prepared brain. The introduction is followed by coverage of parametric image registrations such as landmark based, principal axes based, and optimal affine linear registration. Basic distance measures like sum of squared differences, correlation, and mutual information are also discussed. The next section is devoted to state-of-the-art non-parametric image registrations where general variational based framework for image registration is presented and used to describe and compare well-known and new image registration techniques. Finally, efficient numerical schemes for the underlying partial differential equations are presented and discussed. This text treats the basic mathematical principles, including aspects from approximation theory, image processing, numerics, partial differential equations, and statistics, with a strong focus on numerical methods in image processing. Providing a systematic and general framework for image registration, the book not only presents state-of-the-art concepts but also summarises and classifies the numerous techniques to be found in the literature.

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

Whenever images taken at different times, from different viewpoints, and/or by different sensors need to be compared, merged, or integrated, image registration is required. Registration, also known as alignment, fusion, or warping, is the process of transforming data into a common reference frame. This book provides an overview of state-of-the-art registration techniques from theory to practice, numerous exercises, and via a supplementary Web page, free access to FAIR.m, a package that is based on the MATLAB software environment.

This book constitutes the refereed proceedings of the 4th International Workshop on Biomedical Image Registration, WBIR 2010, held in Lübeck, Germany, in July 2010. The 17 revised full papers and 7 revised poster papers presented were carefully reviewed and selected for inclusion in the book. The papers cover all areas of biomedical image registration and are organized in topical sections on biomedical applications, evaluation, methods of registration, and model based registration.