Biomedical Image Understanding

Name: Biomedical Image Understanding | Methods and Applications
Brand: Wiley
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Methods and Applications

Joo-Hwee Lim Sim-Heng Ong Wei Xiong(Autor*in)

Wiley (Verlag)

Erschienen am 9. Februar 2015

496 Seiten

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Inhalt

Cover
Contents
List of Contributors
Preface
Acronyms
Part I Introduction
Chapter 1 Overview of Biomedical Image Understanding Methods
1.1 Segmentation and Object Detection
1.1.1 Methods Based on Image Processing Techniques
1.1.2 Methods Using Pattern Recognition and Machine Learning Algorithms
1.1.3 Model and Atlas-Based Segmentation
1.1.4 Multispectral Segmentation
1.1.5 User Interactions in Interactive Segmentation Methods
1.1.6 Frontiers of Biomedical Image Segmentation
1.2 Registration
1.2.1 Taxonomy of Registration Methods
1.2.2 Frontiers of Registration for Biomedical Image Understanding
1.3 Object Tracking
1.3.1 Object Representation
1.3.2 Feature Selection for Tracking
1.3.3 Object Tracking Technique
1.3.4 Frontiers of Object Tracking
1.4 Classification
1.4.1 Feature Extraction and Feature Selection
1.4.2 Classifiers
1.4.3 Unsupervised Classification
1.4.4 Classifier Combination
1.4.5 Frontiers of Pattern Classification for Biomedical Image Understanding
1.5 Knowledge-Based Systems
1.5.1 Semantic Interpretation and Knowledge-Based Systems
1.5.2 Knowledge-Based Vision Systems
1.5.3 Knowledge-Based Vision Systems in Biomedical Image Analysis
1.5.4 Frontiers of Knowledge-Based Systems
References
Part II Segmentation and Object Detection
Chapter 2 Medical Image Segmentation and its Application in Cardiac MRI
2.1 Introduction
2.2 Background
2.2.1 Active Contour Models
2.2.2 Parametric and Nonparametric Contour Representation
2.2.3 Graph-Based Image Segmentation
2.2.4 Summary
2.3 Parametric Active Contours -- The Snakes
2.3.1 The Internal Spline Energy E int
2.3.2 The Image-Derived Energy E img
2.3.3 The External Control Energy E con
2.3.4 Extension of Snakes and Summary of Parametric Active Contours
2.4 Geometric Active Contours -- The Level Sets
2.4.1 Variational Level Set Methods
2.4.2 Region-Based Variational Level Set Methods
2.4.3 Summary of Level Set Methods
2.5 Graph-Based Methods -- The Graph Cuts
2.5.1 Basic Graph Cuts Formulation
2.5.2 Patch-Based Graph Cuts
2.5.3 An Example of Graph Cuts
2.5.4 Summary of Graph Cut Methods
2.6 Case Study: Cardiac Image Segmentation Using A Dual Level Sets Model
2.6.1 Introduction
2.6.2 Method
2.6.3 Experimental Results
2.6.4 Conclusion of the Case Study
2.7 Conclusion and Near-Future Trends
References
Chapter 3 Morphometric Measurements of the Retinal Vasculature in Fundus Images With Vampire
3.1 Introduction
3.2 Assessing Vessel Width
3.2.1 Previous Work
3.2.2 Our Method
3.2.3 Results
3.2.4 Discussion
3.3 Artery or Vein?
3.3.1 Previous Work
3.3.2 Our Solution
3.3.3 Results
3.3.4 Discussion
3.4 Are My Program's Measurements Accurate?
3.4.1 Discussion
References
Chapter 4 Analyzing Cell and Tissue Morphologies Using Pattern Recognition Algorithms
4.1 Introduction
4.2 Texture Segmentation of Endometrial Images Using the Subspace Mumford--Shah Model
4.2.1 Subspace Mumford--Shah Segmentation Model
4.2.2 Feature Weights
4.2.3 Once-and-For-All Approach
4.2.4 Results
4.3 Spot Clustering for Detection of Mutants in Keratinocytes
4.3.1 Image Analysis Framework
4.3.2 Results
4.4 Cells and Nuclei Detection
4.4.1 Model
4.4.2 Neural Cells and Breast Cancer Cells Data
4.4.3 Performance Evaluation
4.4.4 Robustness Study
4.4.5 Results
4.5 Geometric Regional Graph Spectral Feature
4.5.1 Conversion of Image Patches into Region Signatures
4.5.2 Comparing Region Signatures
4.5.3 Classification of Region Signatures
4.5.4 Random Masking and Object Detection
4.5.5 Results
4.6 Mitotic Cells in the H&E Histopathological Images of Breast Cancer Carcinoma
4.6.1 Mitotic Index Estimation
4.6.2 Mitotic Candidate Selection
4.6.3 Exclusive Independent Component Analysis (XICA)
4.6.4 Classification Using Sparse Representation
4.6.5 Training and Testing Over Channels
4.6.6 Results
4.7 Conclusions
References
Part III Registration and Matching
Chapter 5 3D Nonrigid Image Registration by Parzen-Window-Based Normalized Mutual Information and its Application on Mr-Guided Microwave Thermocoagulation of Liver Tumors
5.1 Introduction
5.2 Parzen-Window-Based Normalized Mutual Information
5.2.1 Definition of Parzen-Window Method
5.2.2 Parzen-Window-Based Estimation of Joint Histogram
5.2.3 Normalized Mutual Information and its Derivative
5.3 Analysis of Kernel Selection
5.3.1 The Designed Kernel
5.3.2 Comparison in Theory
5.3.3 Comparison by Experiments
5.4 Application on MR-Guided Microwave Thermocoagulation of Liver Tumors
5.4.1 Introduction of MR-Guided Microwave Thermocoagulation of Liver Tumors
5.4.2 Nonrigid Registration by Parzen-Window-Based Mutual Information
5.4.3 Evaluation on Phantom Data
5.4.4 Evaluation on Clinical Cases
5.5 Conclusion
Acknowledgements
References
Chapter 6 2D/3D Image Registration For Endovascular Abdominal Aortic Aneurysm (AAA) Repair
6.1 Introduction
6.2 Background
6.2.1 Image Modalities
6.2.2 2D/3D Registration Framework
6.2.3 Feature-Based Registration
6.2.4 Intensity-Based Registration
6.2.5 Number of Imaging Planes
6.2.6 2D/3D Registration for Endovascular AAA Repair
6.3 Smart Utilization of Two X-Ray Images for Rigid-Body 2D/3D Registration
6.3.1 2D/3D Registration: Challenges in EVAR
6.3.2 3D Image Processing and DRR Generation
6.3.3 2D Image Processing
6.3.4 Similarity Measure
6.3.5 Optimization
6.3.6 Validation
6.4 Deformable 2D/3D Registration
6.4.1 Problem Formulation
6.4.2 Graph-Based Difference Measure
6.4.3 Length Preserving Term
6.4.4 Smoothness Term
6.4.5 Optimization
6.4.6 Validation
6.5 Visual Check of Patient Movement Using Pelvis Boundary Detection
6.6 Discussion and Conclusion
References
Part IV Object Tracking
Chapter 7 Motion Tracking in Medical Images
7.1 Introduction
7.1.1 Point-Based Tracking
7.1.2 Silhouette-Based Tracking
7.1.3 Kernel-Based Tracking
7.2 Background
7.2.1 Point-Based Tracking
7.2.2 Silhouette-Based Tracking
7.2.3 Kernel-Based Tracking
7.2.4 Summary
7.3 Bayesian Tracking Methods
7.3.1 Kalman Filters
7.3.2 Particle Filters
7.3.3 Summary of Bayesian Tracking Methods
7.4 Deformable Models
7.4.1 Mathematical Foundations of Deformable Models
7.4.2 Energy-Minimizing Deformable Models
7.4.3 Probabilistic Deformable Models
7.4.4 Summary of Deformable Models
7.5 Motion Tracking Based on the Harmonic Phase Algorithm
7.5.1 HARP Imaging
7.5.2 HARP Tracking
7.5.3 Summary
7.6 Case Study: Pseudo Ground Truth-Based Nonrigid Registration of MRI for Tracking the Cardiac Motion
7.6.1 Data Fidelity Term
7.6.2 Spatial Smoothness Constraint
7.6.3 Temporal Smoothness Constraint
7.6.4 Energy Minimization
7.6.5 Preliminary Results
7.6.6 Nonrigid Registration of Myocardial Perfusion MRI
7.6.7 Experimental Results
7.7 Discussion
7.8 Conclusion and Near-Future Trends
References
Part V Classification
Chapter 8 Blood Smear Analysis, Malaria Infection Detection, and Grading from Blood Cell Images
8.1 Introduction
8.2 Pattern Classification Techniques
8.2.1 Supervised and Nonsupervised Learning
8.2.2 Bayesian Decision Theory
8.2.3 Clustering
8.2.4 Support Vector Machines
8.3 GWA Detection
8.3.1 Image Analysis
8.3.2 Association between the Object Area and the Number of Cells Per Object
8.3.3 Clump Splitting
8.3.4 Clump Characterization
8.3.5 Classification
8.4 Dual-Model-Guided Image Segmentation and Recognition
8.4.1 Related Work
8.4.2 Strategies and Object Functions
8.4.3 Endpoint Adjacency Map Construction and Edge Linking
8.4.4 Parsing Contours and Their Convex Hulls
8.4.5 A Recursive and Greedy Splitting Approach
8.4.6 Incremental Model Updating and Bayesian Decision
8.5 Infection Detection and Staging
8.5.1 Related Work
8.5.2 Methodology
8.6 Experimental Results
8.6.1 GWA Classification
8.6.2 RBC Segmentation
8.6.3 RBC Classification
8.7 Summary
References
Chapter 9 Liver Tumor Segmentation Using SVM Framework and Pathology Characterization Using Content-Based Image Retrieval
9.1 Introduction
9.2 Liver Tumor Segmentation Under a Hybrid SVM Framework
9.2.1 Fundamentals of SVM for Classification
9.2.2 SVM Framework for Liver Tumor Segmentation and the Problems
9.2.3 A Three-Stage Hybrid SVM Scheme for Liver Tumor Segmentation
9.2.4 Experiment
9.2.5 Evaluation Metrics
9.2.6 Results
9.3 Liver Tumor Characterization by Content-Based Image Retrieval
9.3.1 Existing Work and the Rationale of Using CBIR
9.3.2 Methodology Overview and Preprocessing
9.3.3 Tumor Feature Representation
9.3.4 Similarity Query and Tumor Pathological Type Prediction
9.3.5 Experiment
9.3.6 Results
9.4 Discussion
9.4.1 About Liver Tumor Segmentation Using Machine Learning
9.4.2 About Liver Tumor Characterization Using CBIR
9.5 Conclusion
References
Chapter 10 Benchmarking Lymph Node Metastasis Classification for Gastric Cancer Staging
10.1 Introduction
10.1.1 Introduction of GSI-CT
10.1.2 Imaging Findings of Gastric Cancer
10.2 Related Feature Selection, Metric Learning, and Classification Methods
10.2.1 Feature Extraction
10.2.2 KNN
10.2.3 Feature Selection
10.2.4 AdaBoost and EAdaBoost Algorithms
10.3 Preprocessing Method for GSI-CT Data
10.3.1 Data Acquisition for GSI-CT Data
10.3.2 Univariate Analysis
10.4 Classification Results For GSI-CT Data of Gastric Cancer
10.4.1 Experimental Results of mRMR-KNN
10.4.2 Experimental Results of SFS-KNN
10.4.3 Experimental Results of Metric Learning
10.4.4 Experiments Results of AdaBoost and EAdaBoost
10.4.5 Experiment Analysis
10.5 Conclusion and Future Work
Acknowledgment
References
Part VI Knowledge-Based Systems
Chapter 11 The Use of Knowledge in Biomedical Image Analysis
11.1 Introduction
11.2 Data, Information, and Knowledge?
11.2.1 Data Versus Information
11.2.2 Knowledge Versus Information
11.3 What Kind of Information/Knowledge Can be Introduced?
11.4 How to Introduce Information in Computer Vision Systems?
11.4.1 Nature of Prior Information/Knowledge
11.4.2 Frameworks Allowing Prior Information Introduction
11.5 Conclusion
References
Chapter 12 Active Shape Model for Contour Detection of Anatomical Structure
12.1 Introduction
12.2 Background
12.2.1 Free-Form Deformable Models
12.2.2 Parametrically Deformable Models
12.3 Methodology
12.3.1 Point Distribution Model
12.3.2 Active Shape Model (ASM)
12.3.3 A Modified ASM
12.4 Applications
12.4.1 Boundary Detection of Optic Disk
12.4.2 Lens Structure Detection
12.5 Summary
Acknowledgment
References
Index
Supplemental Images
EULA

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