Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches (The MICCAI Society book Series) - Tapa dura

Acerca del autor

S. Kevin Zhou, PhD is dedicated to research on medical image computing, especially analysis and reconstruction, and its applications in real practices. Currently, he is a Distinguished Professor and Founding Executive Dean of School of Biomedical Engineering, University of Science and Technology of China (USTC) and directs the Center for Medical Imaging, Robotics, Analytic Computing and Learning (MIRACLE). Dr. Zhou was a Principal Expert and a Senior R&D Director at Siemens Healthcare Research. He has been elected as a fellow of AIMBE, IAMBE, IEEE, MICCAI and NAI and serves the MICCAI society as a board member and treasurer..

De la contraportada

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

Key Features:

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