Guide to Medical Image Analysis / Advances in Computer Vision and Pattern Recognition (PDF)
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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.
- Autor: Klaus D. Toennies
- 2017, 2nd ed. 2017, 589 Seiten, Englisch
- Verlag: Springer-Verlag GmbH
- ISBN-10: 1447173201
- ISBN-13: 9781447173205
- Erscheinungsdatum: 29.03.2017
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
- Dateiformat: PDF
- Größe: 18 MB
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- Vorlesefunktion
“The book is well written and accurate. The author states that he has made a number of additions and corrections in this new edition; the result is very good. … it’s well suited as a textbook for medical professionals. I am evaluating it for adoption in a medical imaging course, and would recommend it to those in the medical field who want a detailed discussion of medical image analysis.” (Computing Reviews, October, 2017)
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