Medical Image Recognition, Segmentation and Parsing: Machine Learning and Multiple Object Approaches

  • 11h 6m
  • S. Kevin Zhou
  • Elsevier Science and Technology Books, Inc.
  • 2016

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

About the Author

Dr. S. Kevin Zhou is currently a Principal Key Expert Scientist of Medical Image Analytics, leading a team of full time research scientists and intern students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in machine learning and computer vision and their applications to medical image recognition and parsing, face recognition and modeling, etc. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year.

In this Book

  • Foreword
  • Introduction to Medical Image Recognition, Segmentation, and Parsing
  • A Survey of Anatomy Detection
  • Robust Multi-Landmark Detection Based on Information Theoretic Scheduling
  • Landmark Detection Using Submodular Functions
  • Random Forests for Localization of Spinal Anatomy
  • Integrated Detection Network for Multiple Object Recognition
  • Organ Detection Using Deep Learning
  • A Probabilistic Framework for Multiple Organ Segmentation Using Learning Methods and Level Sets
  • LOGISMOS—A Family of Graph-Based Optimal Image Segmentation Methods
  • A Context Integration Framework for Rapid Multiple Organ Parsing
  • Multiple-Atlas Segmentation in Medical Imaging
  • An Overview of the Multi-Object Geometric Deformable Model Approach in Biomedical Imaging
  • Robust and Scalable Shape Prior Modeling via Sparse Representation and Dictionary Learning
  • Semantic Parsing of Brain MR Images
  • Parsing of the Lungs and Airways
  • Aortic and Mitral Valve Modeling from Multi-Modal Image Data
  • Model-Based 3D Cardiac Image Segmentation with Marginal Space Learning
  • Spine Disk and RIB Centerline Parsing
  • Data-Driven Detection and Segmentation of Lymph Nodes
  • Polyp Segmentation on CT Colonography
  • Detect Cells and Cellular Behaviors in Phase Contrast Microscopy Images
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