Image Synthesis
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Image Synthesis
Author: Nadia Magnenat-Thalmann
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Image Synthesis: Theory and Practice is the first book completely dedicated to the numerous techniques of image synthesis. Both theoretical and practical aspects are treated in detail. Numerous impressive computer-generated images are used to explain the most advanced techniques in image synthesis. The book contains a detailed description of the most fundamental algorithms; other less important algorithms are summarized or simply listed. This volume is also a unique handbook of mathematical formulae for image synthesis. The four first chapters of the book survey the basic techniques of computer graphics which play an important role in the design of an image: geometric models, image and viewing transformations, curves and surfaces and solid modeling techniques. In the next chapters, each major topic in image synthesis is presented. The first important problem is the detection and processing of visible surfaces, then two chapters are dedicated to the central problem of light and illumination. As aliasing is a major problem in image rendering, the fundamental antialiasing and motion blur techniques are explained. The most common shadow algorithms are then presented as well as techniques for producing soft shadows and penumbrae. In the last few years, image rendering has been strongly influenced by ray tracing techniques. For this reason, two chapters are dedicated to this important approach. Then a chapter is completely dedicated to fractals from the formal Mandelbrot theory to the recursive subdivision approaches. Natural phenomena present a particularly difficult challenge in image synthesis. For this reason, a large portion of the book is devoted to latest methods to simulate these phenomena: particle systems, scalar fields, volume density scattering models. Various techniques are also described for representing terrains, mountains, water, waves, sky, clouds, fog, fire, trees, and grass. Several techniques for combining images are also explained: adaptive rendering, montage and composite methods. The last chapter presents in detail the MIRALab image synthesis software.
Biomedical Image Synthesis and Simulation
Biomedical Image Synthesis and Simulation: Methods and Applications presents the basic concepts and applications in image-based simulation and synthesis used in medical and biomedical imaging. The first part of the book introduces and describes the simulation and synthesis methods that were developed and successfully used within the last twenty years, from parametric to deep generative models. The second part gives examples of successful applications of these methods. Both parts together form a book that gives the reader insight into the technical background of image synthesis and how it is used, in the particular disciplines of medical and biomedical imaging. The book ends with several perspectives on the best practices to adopt when validating image synthesis approaches, the crucial role that uncertainty quantification plays in medical image synthesis, and research directions that should be worth exploring in the future. - Gives state-of-the-art methods in (bio)medical image synthesis - Explains the principles (background) of image synthesis methods - Presents the main applications of biomedical image synthesis methods
Medical Image Synthesis
Image synthesis across and within medical imaging modalities is an active area of research with broad applications in radiology and radiation oncology. This book covers the principles and methods of medical image synthesis, along with state-of-the-art research. First, various traditional non-learning-based, traditional machine-learning-based, and recent deep-learning-based medical image synthesis methods are reviewed. Second, specific applications of different inter- and intra-modality image synthesis tasks and of synthetic image-aided segmentation and registration are introduced and summarized, listing and highlighting the proposed methods, study designs, and reported performances with the related clinical applications of representative studies. Third, the clinical usages of medical image synthesis, such as treatment planning and image-guided adaptive radiotherapy, are discussed. Last, the limitations and current challenges of various medical synthesis applications are explored, along with future trends and potential solutions to solve these difficulties. The benefits of medical image synthesis have sparked growing interest in a number of advanced clinical applications, such as magnetic resonance imaging (MRI)-only radiation therapy treatment planning and positron emission tomography (PET)/MRI scanning. This book will be a comprehensive and exciting resource for undergraduates, graduates, researchers, and practitioners.