Guide to Medical Image Analysis

Guide to Medical Image Analysis

Practical in approach, this book describes a range of common imaging techniques, reconstruction techniques and image artifacts, reviews image enhancement methods, discusses image transfer and archiving, and includes exercises and a glossary of abbreviations.

Analysis of medical imaging poses special challenges distinct from traditional image analysis. Furthermore, the analysis must fit into the clinical workflow within which it has been requested.

This important guide/reference presents a comprehensive overview of medical image analysis. Highly practical in its approach, the text is uniquely structured by potential applications, supported by exercises throughout. Each of the key concepts are introduced in a concise manner, allowing the reader to understand the interdependencies between them before exploring the deeper details and derivations.

Topics and features:

Presents learning objectives, exercises and concluding remarks in each chapter, in addition to a glossary of abbreviationsDescribes a range of common imaging techniques, reconstruction techniques and image artefactsDiscusses the archival and transfer of images, including the HL7 and DICOM standardsPresents a selection of techniques for the enhancement of contrast and edges, for noise reduction and for edge-preserving smoothingExamines various feature detection and segmentation techniques, together with methods for computing a registration or normalisation transformationExplores object detection, as well as classification based on segment attributes such as shape and appearanceReviews the validation of an analysis methodIncludes appendices on Markov random field optimization, variational calculus and principal component analysisThis easy-to-follow, classroom-tested textbook is ideal for undergraduate and graduate courses on medical image analysis and related subjects - with possible course outlines suggested in the Preface. The work can also be used as a self-study guide for professionals in medical imaging technology, and for computer scientists and engineers wishing to specialise in medical applications.

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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.

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Inhaltsangabe

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
Detection and Segmentation by Shape and Appearance
Classification and Clustering
Validation
Optimisation of Markov Random Fields
Variational Calculus
Principal Component Analysis
References