@article{780646,
      recid = {780646},
      author = {Toennies, Klaus D.,},
      title = {Guide to medical image analysis : methods and algorithms  /},
      pages = {1 online resource (xxii, 589 pages) :},
      abstract = {This comprehensive guide provides a uniquely practical,  application-focused introduction to medical image analysis.  The text presents a concise examination of each of the key  concepts, enabling the reader to understand the  interdependencies between them before delving deeper into  the derivations and technical details. 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, in  addition to a glossary of abbreviations 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 (NEW) Examines  analysis methods in view of image-based guidance in the  operating room, designed to aid the operator in adapting  their intervention during an operation (NEW) Discusses the  use of deep convolutional networks for segmentation and  labeling tasks, describing how this network architecture  differs from multi-layer perceptrons (NEW) Includes  appendices on Markov random field optimization, variational  calculus and principal component analysis This  clearly-written guide/reference serves as a  classroom-tested textbook for courses on medical image  processing and analysis, with suggestions for course  outlines supplied in the preface. Professionals in medical  imaging technology, as well as computer scientists and  electrical engineers specializing in medical applications,  will also find the book an ideal resource for self-study.},
      url = {http://library.usi.edu/record/780646},
      doi = {https://doi.org/10.1007/978-1-4471-7320-5},
}