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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
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 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
This 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.