Medical image recognition segmentation and parsing machine learning and multiple object approaches 1st Edition by Zhou, Kevin 0128026766 9780128025819 9780128026762

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ISBN-10 : 0128026766 

ISBN-13 :   9780128025819 9780128026762

Author :  Zhou, Kevin 

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.

Medical image recognition, segmentation and parsing : machine learning and multiple object approaches 1st Table of contents:

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

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