Medical Image Recognition, Segmentation and Parsing

Methods, Theories and Applications

By: S. Kevin Zhou

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Hardcover | 8 December 2015 | Edition Number 1

At a Glance

Hardcover
542 Pages

Dimensions(cm)
24.13 x 19.68 x 2.54

Hardcover

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This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image.

Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects.

Learn:

Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects
Methods and theories for medical image recognition, segmentation and parsing of multiple objects
Efficient and effective machine learning solutions based on big datasets
Selected applications of medical image parsing using proven algorithms

Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects
Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets
Includes algorithms for recognizing and parsing of known anatomies for practical applications

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