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Guide to Medical Image Analysis

Methods and Algorithms

By Klaus D. Toennies

Guide to Medical Image Analysis Cover Image

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.

Full Description

  • ISBN13: 978-1-4471-2750-5
  • 488 Pages
  • User Level: Students
  • Publication Date: February 4, 2012
  • Available eBook Formats: PDF
  • eBook Price: $89.95
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Full Description
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.
Table of Contents

Table of Contents

  1. The Analysis of Medical Images.
  2. Digital Image Acquisition.
  3. Image Storage and Transfer.
  4. Image Enhancement.
  5. Feature Detection.
  6. Segmentation: Principles and Basic Techniques.
  7. Segmentation in Feature Space.
  8. Segmentation as a Graph Problem.
  9. Active Contours and Active Surfaces.
  10. Registration and Normalization.
  11. Detection and Segmentation by Shape and Appearance.
  12. Classification and Clustering.
  13. Validation.
  14. Optimisation of Markov Random Fields.
  15. Variational Calculus.
  16. Principal Component Analysis.
  17. References.
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