Medical Image Recognition, Segmentation and Parsing

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

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Description

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.

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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
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Product details

  • Hardback | 542 pages
  • 191 x 235 x 33.02mm | 1,800g
  • Academic Press Inc
  • San Diego, United States
  • English
  • 0128025816
  • 9780128025819

Table of contents

Preface Chapter 1 Introduction to Medical Image Recognition and Parsing Chapter 2 Discriminative Anatomy Detection: Classification vs. Regression Chapter 3: Information Theoretic Landmark Detection Chapter 4: Submodular Landmark Detection Chapter 5: Random Forests for Anatomy Recognition Chapter 6: Integrated Detection Network for Multiple Object Recognition Chapter 7: Optimal Graph-Based Method for Multi-Object Segmentation Chapter 8: Parsing of Multiple Organs Using Learning Method and Level Sets Chapter 9: Context Integration for Rapid Multiple Organ Parsing Chapter 10: Multi-Atlas Methods and Label Fusion Chapter 11: Multi-Compartment Segmentation Framework Chapter 12: Deformable Segmentation via Sparse Representation and Dictionary Learning Chapter 13: Simultaneous Nonrigid Registration, Segmentation, and Tumor Detection Chapter 14: Whole Brain Anatomical Structure Parsing Chapter 15: Aortic and Mitral Valve Segmentation Chapter 16: Parsing of Heart, Chambers and Coronary Vessels Chapter 17: Spine Segmentation Chapter 18: Parsing of Rib and Knee Bones Chapter 19: Lymph Node Segmentation Chapter 20: Polyp Segmentation from CT Colonoscopy
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About S. Kevin Zhou

S. Kevin Zhou, Ph.D. is currently a Principal Key Expert Scientist at Siemens Healthcare Technology Center, leading a team of full time research scientists and students dedicated to researching and developing innovative solutions for medical and industrial imaging products. His research interests lie in computer vision and machine/deep learning and their applications to medical image analysis, face recognition and modeling, etc. He has published over 150 book chapters and peer-reviewed journal and conference papers, registered over 250 patents and inventions, written two research monographs, and edited three books. He has won multiple technology, patent and product awards, including R&D 100 Award and Siemens Inventor of the Year. He is an editorial board member for Medical Image Analysis journal and a fellow of American Institute of Medical and Biological Engineering (AIMBE).
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