Medical Image Recognition, Segmentation and Parsing - 1st Edition - ISBN: 9780128025819, 9780128026762

Medical Image Recognition, Segmentation and Parsing

1st Edition

Machine Learning and Multiple Object Approaches

Authors: S. Kevin Zhou
eBook ISBN: 9780128026762
Hardcover ISBN: 9780128025819
Imprint: Academic Press
Published Date: 2nd December 2015
Page Count: 542
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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.

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

  • 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

Readership

Industry practitioners and university researchers in medical imaging.

Table of Contents

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  • Foreword
  • Acknowledgments
  • Chapter 1: Introduction to Medical Image Recognition, Segmentation, and Parsing
    • Abstract
    • 1.1 Introduction
    • 1.2 Challenges and Opportunities
    • 1.3 Rough-to-Exact Object Representation
    • 1.4 Simple-to-Complex Probabilistic Modeling
    • 1.5 Medical Image Recognition Using Machine Learning Methods
    • 1.6 Medical Image Segmentation Methods
    • 1.7 Conclusions
    • Recommended Notations
  • Part 1: Automatic Recognition and Detection Algorithms
    • Chapter 2: A Survey of Anatomy Detection
      • Abstract
      • 2.1 Introduction
      • 2.2 Methods for Detecting an Anatomy
      • 2.3 Methods for Detecting Multiple Anatomies
      • 2.4 Conclusions
    • Chapter 3: Robust Multi-Landmark Detection Based on Information Theoretic Scheduling
      • Abstract
      • 3.1 Introduction
      • 3.2 Literature Review
      • 3.3 Methods
      • 3.4 Applications
      • 3.5 Conclusion
    • Chapter 4: Landmark Detection Using Submodular Functions
      • Abstract
      • 4.1 Introduction
      • 4.2 Multiple Landmark Detection
      • 4.3 Finding the Anchor Landmark
      • 4.4 Coarse-to-Fine Detection
      • 4.5 Discussion
      • 4.6 Summary
    • Chapter 5: Random Forests for Localization of Spinal Anatomy
      • Abstract
      • 5.1 Introduction
      • 5.2 Anatomy Localization Using Random Forests
      • 5.3 Experimental Comparison
      • 5.4 Conclusion
    • Chapter 6: Integrated Detection Network for Multiple Object Recognition
      • Abstract
      • 6.1 Introduction
      • 6.2 Independent Multiobject Recognition
      • 6.3 Sequential Sampling for Multiobject Recognition
      • 6.4 Applications
      • 6.5 Conclusions
    • Chapter 7: Organ Detection Using Deep Learning
      • Abstract
      • Acknowledgments
      • 7.1 Introduction
      • 7.2 Related Literature
      • 7.3 Methods
      • 7.4 Experiments
      • 7.5 Conclusions
  • Part 2: Automatic Segmentation and Parsing Algorithms
    • Chapter 8: A Probabilistic Framework for Multiple Organ Segmentation Using Learning Methods and Level Sets
      • Abstract
      • 8.1 Introduction
      • 8.2 Literature Review
      • 8.3 Proposed Method
      • 8.4 Experimental Results
      • 8.5 Conclusions
    • Chapter 9: LOGISMOS: A Family of Graph-Based Optimal Image Segmentation Methods
      • Abstract
      • Acknowledgments
      • 9.1 Introduction
      • 9.2 Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces
      • 9.3 Multiobject Multisurface LOGISMOS for Knee Joint Segmentation
      • 9.4 Multisurface Multiimage Co-Segmentation: Retinal OCT
      • 9.5 Complex Multisurface Geometry: LOGISMOS-B for Brain Cortex
      • 9.6 Future Directions
    • Chapter 10: A Context Integration Framework for Rapid Multiple Organ Parsing
      • Abstract
      • 10.1 Introduction
      • 10.2 Related Literature
      • 10.3 Methods
      • 10.4 Object Context
      • 10.5 Automatic Mesh Vertex Selection
      • 10.6 Incomplete Annotations
      • 10.7 Experiments
      • 10.8 Conclusions
    • Chapter 11: Multiple-Atlas Segmentation in Medical Imaging
      • Abstract
      • 11.1 Introduction
      • 11.2 Atlas Selection
      • 11.3 Image Registration
      • 11.4 Label Fusion
      • 11.5 Conclusions
    • Chapter 12: An Overview of the Multi-Object Geometric Deformable Model Approach in Biomedical Imaging
      • Abstract
      • Acknowledgments
      • 12.1 Introduction
      • 12.2 Methods
      • 12.3 Segmentation of a Multi-Object Hand
      • 12.4 Applications
      • 12.5 Discussion and Conclusion
    • Chapter 13: Robust and Scalable Shape Prior Modeling via Sparse Representation and Dictionary Learning
      • Abstract
      • 13.1 Introduction
      • 13.2 Related Work
      • 13.3 Segmentation Framework
      • 13.4 Sparse Shape Composition
      • 13.5 Dictionary Learning for Compact Representations
      • 13.6 Mesh Partition for Local Sparse Shape Composition
      • 13.7 Discussion
  • Part 3: Recognition, Segmentation and Parsing of Specific Objects
    • Chapter 14: Semantic Parsing of Brain MR Images
      • Abstract
      • 14.1 Introduction
      • 14.2 Atlas-Based Segmentation Methods
      • 14.3 Brain Atlases From MR Images
      • 14.4 Conclusions
    • Chapter 15: Parsing of the Lungs and Airways
      • Abstract
      • 15.1 Introduction
      • 15.2 Overview
      • 15.3 Lung and Airway Segmentation
      • 15.4 Airway Tree Parsing
      • 15.5 Lobar Segmentation
      • 15.6 Quantification of Airway Dimensions
      • 15.7 Applications
      • 15.8 Conclusion
    • Chapter 16: Aortic and Mitral Valve Modeling From Multi-Modal Image Data
      • Abstract
      • 16.1 Introduction
      • 16.2 Physiological Model of the Heart Valves
      • 16.3 Patient-Specific Model Parameter Estimation
      • 16.4 Experimental Results
      • 16.5 Conclusions
    • Chapter 17: Model-Based 3D Cardiac Image Segmentation With Marginal Space Learning
      • Abstract
      • Acknowledgments
      • 17.1 Introduction
      • 17.2 Marginal Space Learning for 3D Object Segmentation
      • 17.3 Cardiac Chamber Segmentation
      • 17.4 Great Vessel Segmentation
      • 17.5 Coronary Artery Segmentation
      • 17.6 Experiments
      • 17.7 Conclusions and Future Work
    • Chapter 18: Spine Disk and RIB Centerline Parsing
      • Abstract
      • 18.1 Introduction
      • 18.2 Related Work
      • 18.3 Spine Disk Parsing
      • 18.4 RIB Centerline Parsing
      • 18.5 Conclusions
    • Chapter 19: Data-Driven Detection and Segmentation of Lymph Nodes
      • Abstract
      • 19.1 Introduction
      • 19.2 Related Work
      • 19.3 LN Center Candidate Detection
      • 19.4 Segmentation-Based Verification
      • 19.5 Spatial Prior
      • 19.6 Experiments
      • 19.7 Conclusion
    • Chapter 20: Polyp Segmentation on CT Colonography
      • Abstract
      • Acknowledgments
      • 20.1 Colonic Polyp and Colon Cancer
      • 20.2 CT Colonography
      • 20.3 Computer-Aided Detection and Diagnosis on CTC
      • 20.4 Polyp Segmentation
      • 20.5 Polyp Measurement and Characterization
      • 20.6 Data Acquisition and Validation Experiment
      • 20.7 Results
      • 20.8 Discussion
    • Chapter 21: Detect Cells and Cellular Behaviors in Phase Contrast Microscopy Images
      • Abstract
      • 21.1 Introduction
      • 21.2 Computer Vision Tasks in Analyzing Cell Populations
      • 21.3 Cell Segmentation
      • 21.4 Cellular Behavior Understanding
      • 21.5 Systems for Analyzing Cell Populations in Time-Lapse Imaging
      • 21.6 Open Source Cell Image Sequence Data
  • Index

Details

No. of pages:
542
Language:
English
Copyright:
© Academic Press 2016
Published:
Imprint:
Academic Press
eBook ISBN:
9780128026762
Hardcover ISBN:
9780128025819

About the Author

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).

Affiliations and Expertise

Principal Key Expert, Medical Image Analysis, Siemens Healthcare Technology Center, Princeton, New Jersey, USA