Description
Springer Guide to Medical Image Analysis Methods and Algorithms by Klaus D. Toennies
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._x000D_ Table of contents :- _x000D_
The Analysis of Medical Images_x000D_
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Digital Image Acquisition_x000D_
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Image Storage and Transfer_x000D_
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Image Enhancement_x000D_
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Feature Detection_x000D_
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Segmentation: Principles and Basic Techniques_x000D_
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Segmentation in Feature Space_x000D_
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Segmentation as a Graph Problem_x000D_
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Active Contours and Active Surfaces_x000D_
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Registration and Normalization_x000D_
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Detection and Segmentation by Shape and Appearance_x000D_
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Classification and Clustering_x000D_
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Validation_x000D_
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Optimisation of Markov Random Fields_x000D_
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Variational Calculus_x000D_
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Principal Component Analysis_x000D_
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References_x000D_