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Medical Image Recognition Segmentation And Parsing Machine Learning And Multiple Object Approaches 1st Edition Zhou

  • SKU: BELL-5432716
Medical Image Recognition Segmentation And Parsing Machine Learning And Multiple Object Approaches 1st Edition Zhou
$ 31.00 $ 45.00 (-31%)

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Medical Image Recognition Segmentation And Parsing Machine Learning And Multiple Object Approaches 1st Edition Zhou instant download after payment.

Publisher: Elsevier, Academic Press
File Extension: PDF
File size: 201.33 MB
Pages: 542
Author: Zhou, S. Kevin
ISBN: 9780128025819, 9780128026762, 0128025816, 0128026766
Language: English
Year: 2016
Edition: 1

Product desciption

Medical Image Recognition Segmentation And Parsing Machine Learning And Multiple Object Approaches 1st Edition Zhou by Zhou, S. Kevin 9780128025819, 9780128026762, 0128025816, 0128026766 instant download after payment.

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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