Guide to Medical Image Analysis

This comprehensive guide provides a uniquely practical, application-focused introduction to medical image analysis. The text presents a concise examination of each of the key concepts, enabling the reader to understand the interdependencies between them before delving deeper into the derivations and technical details. This fully updated new edition has been enhanced with material on the latest developments in the field, whilst retaining the original focus on segmentation, classification and registration. Topics and features:

• Describes a range of common imaging techniques, reconstruction techniques and image artifacts, and discusses the archival and transfer of images
• Reviews an expanded selection of techniques for image enhancement, feature detection, feature generation, segmentation, registration, and validation (NEW)
• Examines analysis methods in view of image-based guidance in the operating room, designed to aid the operator in adapting their intervention during an operation (NEW)
• Discusses the use of deep convolutional networks for segmentation and labeling tasks, describing how this network architecture differs from multi-layer perceptrons (NEW)

• Includes appendices on Markov random field optimization, variational calculus and principal component analysis
  This clearly-written guide/reference serves as a classroom-tested textbook for courses on medical image processing and analysis, with suggestions for course outlines supplied in the preface. Professionals in medical imaging technology, as well as computer scientists and electrical engineers specializing in medical applications, will also find the book an ideal resource for self-study.

Auteur

Dr. Klaus D. Toennies is a Professor of Image Processing and Pattern Recognition at the Department of Simulation and Graphics of the Otto-von-Guericke University of Magdeburg, Germany.

Contenu

The Analysis of Medical Images

Digital Image Acquisition

Image Storage and Transfer

Image Enhancement

Feature Detection

Segmentation: Principles and Basic Techniques

Segmentation in Feature Space

Segmentation as a Graph Problem

Active Contours and Active Surfaces

Registration and Normalization

Shape, Appearance and Spatial Relationships

Classification and Clustering

Validation

Appendix