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Medical Image Recognition, Segmentation And Parsing eBook

Machine Learning And Multiple Object Approaches

by S. Kevin Zhou
language: english
Publisher: ELSEVIER SCIENCE, December of 2015 ‧
116,60€
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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

Medical Image Recognition, Segmentation And Parsing

Machine Learning And Multiple Object Approaches

by S. Kevin Zhou

Property Description
ISBN: 9780128026762
Publisher: ELSEVIER SCIENCE
Release Date: December of 2015
Language: English
Format: eBook
File Format and Compatibility:
Categories: eBooks in English > Medicine > General Medicine
EAN: 9780128026762
Acessibilidade: Ver características de acessibilidade indicadas pelo editor