Guide to Medical Image Analysis

This comprehensive guide provides a uniquely practical, application-focused introduction to medical image analysis. 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: presents learning objectives, exercises and concluding remarks in each chapter; 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; examines analysis methods in view of image-based guidance in the operating room (NEW); discusses the use of deep convolutional networks for segmentation and labeling tasks (NEW); includes appendices on Markov random field optimization, variational calculus and principal component analysis.

An in-depth-introduction into medical image analysis, suitable for use as a textbook Provides a detailed discussion on segmentation, classification and registration techniques Presents the methods in the context of their adequate use, based on the constraints necessary for successful application Updated new edition, expanded with additional methods, and coverage of deep convolutional networks Includes supplementary material: sn.pub/extras

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.