Материалы

Biosignal and Biomedical Image Processing
MATLAB-Based Applications John L. Semmlow
Robert Wood Johnson Medical School New Brunswick, New Jersey, U.S.A.
Rutgers University Piscataway, New Jersey, U.S.A.

Biomedical Image Analysis
Description: Computers have become an integral part of medical imaging systems and are used for everything from data acquisition and image generation to image display and analysis. As the scope and complexity of imaging technology steadily increase, more advanced techniques are required to solve the emerging challenges. Biomedical Image Analysis demonstrates the benefits reaped from the application of digital image processing, computer vision, and pattern analysis techniques to biomedical images, such as adding objective strength and improving diagnostic confidence through quantitative analysis. The book focuses on post-acquisition challenges such as image enhancement, detection of edges and objects, analysis of shape, quantification of texture and sharpness, and pattern analysis, rather than on the imaging equipment and imaging techniques. Each chapter addresses several problems associated with imaging or image analysis, outlining the typical processes, then detailing more sophisticated methods directed to the specific problems of interest. Biomedical Image Analysis is useful for senior undergraduate and graduate biomedical engineering students, practicing engineers, and computer scientists working in diverse areas such as telecommunications, biomedical applications, and hospital information systems.

Image Processing for Remote Sensing
Edited by leaders in the field, with contributions by a panel of experts, Image Processing for Remote Sensing explores new and unconventional mathematics methods. The coverage includes the physics and mathematical algorithms of SAR images, a comprehensive treatment of MRF-based remote sensing image classification, statistical approaches for improved classification with the remote sensing data, Wiener filter-based method, and other modern approaches and methods of image processing for remotely sensed data. Each chapter explores a technique for dealing with a specific remote sensing problem. The book offers physical insights on the steps for constructing various digital seismic images. The volume examines image modeling, statistical image classifiers, change detection, independent component analysis, vertex component analysis, image fusion for better classification. It explores unique topics such as accuracy assessment and information-theoretic measure of multiband images and many chapters emphasize issues with synthetic aperture radar (SAR) images. Continued development on imaging sensors creates new opportunities and challenges in image processing for remote sensing. Image Processing for Remote Sensing not only presents the most up to date developments of image processing for remote sensing but also suggests to readers the many challenging problems ahead for further study.

Multi-Sensor Image Fusion and Its Applications
Taking another lesson from nature, the latest advances in image processing technology seek to combine image data from several diverse types of sensors in order to obtain a more accurate view of the scene: very much the same as we rely on our five senses. Multi-Sensor Image Fusion and Its Applications is the first text dedicated to the theory and practice of the registration and fusion of image data, covering such approaches as statistical methods, color-related techniques, model-based methods, and visual information display strategies. After a review of state-of-the-art image fusion techniques, the book provides an overview of fusion algorithms and fusion performance evaluation. The following chapters explore recent progress and practical applications of the proposed techniques to solving problems in such areas as medical diagnosis, surveillance and biometric systems, remote sensing, nondestructive evaluation, blurred image restoration, and image quality assessment. Recognized leaders from industry and academia contribute the chapters, reflecting the latest research trends and providing useful algorithms to aid implementation. Supplying a 28-page full-color insert, Multi-Sensor Image Fusion and Its Applications clearly demonstrates the benefits and possibilities of this revolutionary development. It provides a solid knowledge base for applying these cutting-edge techniques to new challenges and creating future advances.

Natural Image Statistics: A Probablistic Approach to Early Computational Vision with Matlab code
One of the most successful frameworks in computational neuroscience is modelling visual processing using the statistical structure of natural images. In this framework, the visual system of the brain constructs a model of the statistical regularities of the incoming visual data. This enables the visual system to perform efficient probabilistic inference. The same framework is also very useful in engineering applications such as image processing and computer vision. This book is the first comprehensive introduction to the multidisciplinary field of natural image statistics. The book starts with a review of background material in signal processing and neuroscience, which makes it accessible to a wide audience. The book then explains both the basic theory and the most recent advances in a coherent and user-friendly manner. This structure, together with the included exercises and computer assignments, also make it an excellent textbook. Natural Image Statistics is a timely and valuable resource for advanced students and researchers in any discipline related to vision, such as neuroscience, computer science, psychology, electrical engineering, cognitive science or statistics.