The visualization, construction, and reconstruction of multidimensional images are of intense interest in science and engineering today, and discrete tomography—which deals with the special case in which the object to be reconstructed has a small number of possible values—offers some significant new analytical and computational tools. Discrete Tomography: Foundations, Algorithms, and Applications provides a critical survey of new methods, algorithms, and select applications that are the foundations of multidimensional image construction and reconstruction. The survey chapters, written by leading international authorities, are self-contained adn present the latest research and results in the field. The book covers three main areas: important theoretical results, available algorithms to utilize for reconstruction, and key applications where new results are indicative of greater utility. Following a thorough historical overview of the field, the book provides a journey through the various mathematical and computational problems of discrete tomography. This is followed by a section on numerous algorithmic techniques that can be used to achieve real reconstructions from image projections. Topics and Features: * historical overview and summary chapter * uniqueness and complexity in discrete tomography * probabilistic modeling of discrete images * binary tomography using Gibb priors * discrete tomography on the 3-D torus and crystals * binary steering * 3-D tomographic reconstruction from sparse radiographic data * symbolic projections The book is an essential resource for the latest developments and tools in discrete tomography. Professionals, researchers, and practitioners in mathematics, computer imaging, scientific visualization, computer engineering, and multidimensional image processing will find the book an authoritative guide and reference to current research, methods, and applications.