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Guide to Medical Image Analysis

Methods and Algorithms

  • Textbook
  • © 2012

Overview

Authors:
  1. Klaus D. Toennies

    1. Computer Science Department, ISG, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany

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  • 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
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)

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Table of contents (14 chapters)

  1. Front Matter

    Pages I-XX
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  2. The Analysis of Medical Images

    • Klaus D. Toennies
    Pages 1-19

  3. Digital Image Acquisition

    • Klaus D. Toennies
    Pages 21-82

  4. Image Storage and Transfer

    • Klaus D. Toennies
    Pages 83-109

  5. Image Enhancement

    • Klaus D. Toennies
    Pages 111-146

  6. Feature Detection

    • Klaus D. Toennies
    Pages 147-169

  7. Segmentation: Principles and Basic Techniques

    • Klaus D. Toennies
    Pages 171-209

  8. Segmentation in Feature Space

    • Klaus D. Toennies
    Pages 211-233

  9. Segmentation as a Graph Problem

    • Klaus D. Toennies
    Pages 235-259

  10. Active Contours and Active Surfaces

    • Klaus D. Toennies
    Pages 261-297

  11. Registration and Normalization

    • Klaus D. Toennies
    Pages 299-331

  12. Detection and Segmentation by Shape and Appearance

    • Klaus D. Toennies
    Pages 333-378

  13. Classification and Clustering

    • Klaus D. Toennies
    Pages 379-412

  14. Validation

    • Klaus D. Toennies
    Pages 413-442

  15. Appendix

    • Klaus D. Toennies
    Pages 443-457

  16. Back Matter

    Pages 459-468
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Keywords

About this book

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.

Authors and Affiliations

  • Computer Science Department, ISG, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany

    Klaus D. Toennies

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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