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Guide to Medical Image Analysis
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This book presents a comprehensive overview of medical image analysis. Practical in approach, the text is uniquely structured by potential applications. Features: presents learning objectives, exercises and concluding remarks in each chapter, in addition to a glossary of abbreviations; describes a range of common imaging techniques, reconstruction techniques and image artefacts; discusses the archival and transfer of images, including the HL7 and DICOM standards; presents a selection of techniques for the enhancement of contrast and edges, for noise reduction and for edge-preserving smoothing; examines various feature detection and segmentation techniques, together with methods for computing a registration or normalisation transformation; explores object detection, as well as classification based on segment attributes such as shape and appearance; reviews the validation of an analysis method; includes appendices on Markov random field optimization, variational calculus and principal component analysis.
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Table of Contents

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

About the Author

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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