Guide to Medical Image Analysis (2^nd Ed., Softcover reprint of the original 2nd ed. 2017)
Methods and Algorithms
Advances in Computer Vision and Pattern Recognition Series

This comprehensive guide provides a uniquely practical, application-focused introduction to medical image analysis. This fully updated new edition has been enhanced with material on the latest developments in the field, whilst retaining the original focus on segmentation, classification and registration. Topics and features: presents learning objectives, exercises and concluding remarks in each chapter; describes a range of common imaging techniques, reconstruction techniques and image artifacts, and discusses the archival and transfer of images; reviews an expanded selection of techniques for image enhancement, feature detection, feature generation, segmentation, registration, and validation; examines analysis methods in view of image-based guidance in the operating room (NEW); discusses the use of deep convolutional networks for segmentation and labeling tasks (NEW); includes appendices on Markov random field optimization, variational calculus and principal component analysis.

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 Updated new edition, expanded with additional methods, and coverage of deep convolutional networks Includes supplementary material: sn.pub/extras