	Medical Image Registration (Biomedical Engineering) by Joseph V. Hajnal (Editor), Derek L.G. Hill (Editor), David J. Hawkes (Editor) 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 provides the first comprehensive coverage of this emerging field. This monograph details the theory, technology, and practical implementations in a variety...
	Bio-Implant Interface: Improving Biomaterials and Tissue Reactions by J.E. Ellingsen (Author), S.P. Lyngstadaas (Author) Achieving good clinical outcomes with implanted biomaterials depends upon achieving optimal function, both mechanical and biological, which in turn depends upon integrating advances realized in biological science, material science, and tissue engineering. As these advances push back the frontiers of biomaterial medicine , the control and patterning of bio-implant interface reactions will have a tremendous impact on future design and prospects of implant treatments.Bio-Implant Interface:...
	Biomedical Imaging (Principles and Applications in Engineering, 10) by Karen M. Mudry (Editor), Robert Plonsey (Editor), Joseph D. Bronzino (Editor) Comprised of chapters carefully selected from CRC's best-selling engineering handbooks, volumes in the Principles and Applications in Engineering series provide convenient, economical references sharply focused on particular engineering topics and subspecialties. Culled from the Biomedical Engineering Handbook, Biomedical Imaging provides an overview of the main medical imaging devices and highlights emerging systems. With applications ranging from imaging the whole body to replicating cellular...

EDITORIAL REVIEWS

Product Description
It is essential that differently oriented specialists and students involved in image processing have a firm grasp of the necessary concepts and principles. A single-source reference that can provide this foundation, as well as a thorough explanation of the techniques involved, particularly those found in medical image processing, would be an invaluable resource to have. Medical Image Processing, Reconstruction and Restoration: Concepts and Methods is that resource. It not only explains the general principles and methods of image processing, but also focuses on recent applications specific to medical imaging – providing a theoretical yet clear explanation of underlying generic concepts. The content of this book is divided into three parts: Part I, Images as Multidimensional Signals, provides the introduction to basic image processing theory, explaining it for both analogue and digital image representation. Part II, Imaging Systems as Data Sources, offers an alternative view on imaging modalities, with emphasis placed on analyzing internal signals and (pre)image data that are consequently processed. Part III, Image Processing and Analysis, focuses on such vital image processing topics as tomographic image reconstruction, image fusion, methods of image enhancement, and image restoration techniques. This section also explains concepts of fundamental-level image analysis – detailing local feature analysis, image segmentation, and generalized morphological transforms. It also addresses what is needed within the image processing environment by noting necessary hardware and software and processes for image archiving and communications.

SIMILAR PRODUCTS

	Digital Image Processing (3rd Edition) by Rafael C. Gonzalez (Author), Richard E. Woods (Author) THE leader in the field for more than twenty years, this introduction to basic concepts and methodologies for digital image processing continues its cutting-edge focus on contemporary developments in all mainstream areas of image processing. Completely self-contained, heavily illustrated, and mathematically accessible, it has a scope of application that is not limited to the solution of specialized problems. Digital Image Fundamentals. Image Enhancement in the Spatial Domain. Image...
	Biosignal and Medical Image Processing, Second Edition: MATLAB-Based Applications (Signal Processing and Communications) by John L. Semmlow (Author) A Practical Guide to Signal Processing Methodology Just as a cardiologist can benefit from an oscilloscope-type display of the ECG without a deep understanding of electronics, an engineer can benefit from advanced signal processing tools without always understanding the details of the underlying mathematics. Through the use of extensive MATLAB® examples and problems, Biosignal and Medical Image Processing, Second Edition provides readers with the necessary knowledge to...
	Digital Image Processing Using MATLAB(R) by Rafael C. Gonzalez (Author), Richard E. Woods (Author), Steven L. Eddins (Author) This is the first book that provides a balanced treatment of image processing basics and software principles used in the practical application of image processing. Working in the MATLAB computing environment, it provides a stable, well-supported set of software tools capable of addressing a broad spectrum of applications in digital image processing. The major areas covered include intensity transformations, linear and nonlinear spatial filtering, filtering in the frequency domain, image...
	Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences) by Gilles Aubert (Author), Pierre Kornprobst (Author) Partial differential equations (PDEs) and variational methods were introduced into image processing about fifteen years ago. Since then, intensive research has been carried out. The goals of this book are to present a variety of image analysis applications, the precise mathematics involved and how to discretize them. Thus, this book is intended for two audiences. The first is the mathematical community by showing the contribution of mathematics to this domain. It is also...
	Semi-Supervised Learning (Adaptive Computation and Machine Learning) by Olivier Chapelle (Editor), Bernhard SchÃ¶lkopf (Editor), Alexander Zien (Editor) In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the...