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Medical Image Recognition, Segmentation and Parsing

Machine Learning and Multiple Object Approaches

  • 1st Edition - December 2, 2015
  • Latest edition
  • Author: S. Kevin Zhou
  • Language: English

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 th… Read more

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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

  • 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

Product details

  • Edition: 1
  • Latest edition
  • Published: December 11, 2015
  • Language: English

About the author

SZ

S. Kevin Zhou

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..
Affiliations and expertise
Principal Key Expert, Medical Image Analysis, Siemens Healthcare Technology Center, Princeton, New Jersey, USA

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