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Biomedical Image Understanding: Methods and Applications Vital Source e-bog
Joo-Hwee Lim, Sim-Heng Ong og Wei Xiong
(2015)
John Wiley & Sons
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Biomedical Image Understanding
Methods and Applications
Joo-Hwee Lim, Sim-Heng Ong og Wei Xiong
(2015)
Sprog: Engelsk
John Wiley & Sons, Limited
1.713,00 kr.
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Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: John Wiley & Sons (Februar 2015)
- Forfattere: Joo-Hwee Lim, Sim-Heng Ong og Wei Xiong
- ISBN: 9781118957578
A comprehensive guide to understanding and interpreting digital images in medical and functional applications Biomedical Image Understanding focuses on image understanding and semantic interpretation, with clear introductions to related concepts, in-depth theoretical analysis, and detailed descriptions of important biomedical applications. It covers image processing, image filtering, enhancement, de-noising, restoration, and reconstruction; image segmentation and feature extraction; registration; clustering, pattern classification, and data fusion. With contributions from experts in China, France, Italy, Japan, Singapore, the United Kingdom, and the United States, Biomedical Image Understanding: Addresses motion tracking and knowledge-based systems, two areas which are not covered extensively elsewhere in a biomedical context Describes important clinical applications, such as virtual colonoscopy, ocular disease diagnosis, and liver tumor detection Contains twelve self-contained chapters, each with an introduction to basic concepts, principles, and methods, and a case study or application With over 150 diagrams and illustrations, this bookis an essential resource for the reader interested in rapidly advancing research and applications in biomedical image understanding.
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Detaljer om varen
- Hardback: 496 sider
- Udgiver: John Wiley & Sons, Limited (April 2015)
- Forfattere: Joo-Hwee Lim, Sim-Heng Ong og Wei Xiong
- ISBN: 9781118715154
A comprehensive guide to understanding and interpreting digital images in medical and functional applications
Biomedical Image Understanding focuses on image understanding and semantic interpretation, with clear introductions to related concepts, in-depth theoretical analysis, and detailed descriptions of important biomedical applications. It covers image processing, image filtering, enhancement, de-noising, restoration, and reconstruction; image segmentation and feature extraction; registration; clustering, pattern classification, and data fusion.
With contributions from experts in China, France, Italy, Japan, Singapore, the United Kingdom, and the United States, Biomedical Image Understanding:
- Addresses motion tracking and knowledge-based systems, two areas which are not covered extensively elsewhere in a biomedical context
- Describes important clinical applications, such as virtual colonoscopy, ocular disease diagnosis, and liver tumor detection
- Contains twelve self-contained chapters, each with an introduction to basic concepts, principles, and methods, and a case study or application
With over 150 diagrams and illustrations, this bookis an essential resource for the reader interested in rapidly advancing research and applications in biomedical image understanding.
List of Contributors xv Preface xix Acronyms xxiii
PART I INTRODUCTION 1 1 Overview of Biomedical Image Understanding Methods 3 Wei Xiong, Jierong Cheng, Ying Gu, Shimiao Li and Joo Hwee Lim
1.1 Segmentation and Object Detection 5
1.1.1 Methods Based on Image Processing Techniques 6
1.1.2 Methods Using Pattern Recognition and Machine Learning Algorithms 7
1.1.3 Model and Atlas-Based Segmentation 8
1.1.4 Multispectral Segmentation 9
1.1.5 User Interactions in Interactive Segmentation Methods 10
1.1.6 Frontiers of Biomedical Image Segmentation 11
1.2 Registration 11
1.2.1 Taxonomy of Registration Methods 12
1.2.2 Frontiers of Registration for Biomedical Image Understanding 15
1.3 Object Tracking 16
1.3.1 Object Representation 17
1.3.2 Feature Selection for Tracking 18
1.3.3 Object Tracking Technique 19
1.3.4 Frontiers of Object Tracking 19
1.4 Classification 20
1.4.1 Feature Extraction and Feature Selection 21
1.4.2 Classifiers 22
1.4.3 Unsupervised Classification 23
1.4.4 Classifier Combination 24
1.4.5 Frontiers of Pattern Classification for Biomedical Image Understanding 25
1.5 Knowledge-Based Systems 26
1.5.1 Semantic Interpretation and Knowledge-Based Systems 26
1.5.2 Knowledge-Based Vision Systems 27
1.5.3 Knowledge-Based Vision Systems in Biomedical Image Analysis 28
1.5.4 Frontiers of Knowledge-Based Systems 29 References 29 PARTII SEGMENTATION AND OBJECT DETECTION 47 2 Medical Image Segmentation and its Application in Cardiac MRI 49 Dong Wei, Chao Li, and Ying Sun
2.1 Introduction 50
2.2 Background 51
2.2.1 Active Contour Models 51
2.2.2 Parametric and Nonparametric Contour Representation 52
2.2.3 Graph-Based Image Segmentation 53
2.2.4 Summary 54
2.3 Parametric Active Contours - The Snakes 54
2.3.1 The Internal Spline Energy E int 54
2.3.2 The Image-Derived Energy E img 55
2.3.3 The External Control Energy E con 55
2.3.4 Extension of Snakes and Summary of Parametric Active Contours 57
2.4 Geometric Active Contours - The Level Sets 58
2.4.1 Variational Level Set Methods 58
2.4.2 Region-Based Variational Level Set Methods 60
2.4.3 Summary of Level Set Methods 64
2.5 Graph-Based Methods - The Graph Cuts 65
2.5.1 Basic Graph Cuts Formulation 65
2.5.2 Patch-Based Graph Cuts 66
2.5.3 An Example of Graph Cuts 68
2.5.4 Summary of Graph Cut Methods 72
2.6 Case Study: Cardiac Image Segmentation Using A Dual Level Sets Model 73
2.6.1 Introduction 73
2.6.2 Method 74
2.6.3 Experimental Results 79
2.6.4 Conclusion of the Case Study 81
2.7 Conclusion and Near-Future Trends 81 References 83 3 Morphometric Measurements of the Retinal Vasculature in Fundus Images With Vampire 91 Emanuele Trucco, Andrea Giachetti, Lucia Ballerini, Devanjali Relan, Alessandro Cavinato, and Tom Macgillivray
3.1 Introduction 92
3.2 Assessing Vessel Width 93
3.2.1 Previous Work 93
3.2.2 Our Method 94
3.2.3 Results 95
3.2.4 Discussion 96
3.3 Artery or Vein? 98
3.3.1 Previous Work 98
3.3.2 Our Solution 99
3.3.3 Results 101
3.3.4 Discussion 103
3.4 Are My Program''s Measurements Accurate? 104
3.4.1 Discussion 106 References 107 4 Analyzing Cell and Tissue Morphologies Using Pattern Recognition Algorithms 113 Hwee Kuan Lee, Yan Nei Law, Chao-Hui Huang, and Choon Kong Yap
4.1 Introduction, 113
4.2 Texture Segmentation of Endometrial Images Using the Subspace Mumford-Shah Model 115
4.2.1 Subspace Mumford-Shah Segmentation Model 116
4.2.2 Feature Weights 118
4.2.3 Once-and-For-All Approach 119
4.2.4 Results 119
4.3 Spot Clustering for Detection of Mutants in Keratinocytes 120
4.3.1 Image Analysis Framework 123
4.3.2 Results 124
4.4 Cells and Nuclei Detection 124
4.4.1 Model 125
4.4.2 Neural Cells and Breast Cancer Cells Data 127
4.4.3 Performance Evaluation 127
4.4.4 Robustness Study 127
4.4.5 Results 128
4.5 Geometric Regional Graph Spectral Feature 134
4.5.1 Conversion of Image Patches into Region Signatures 134
4.5.2 Comparing Region Signatures 135
4.5.3 Classification of Region Signatures 136
4.5.4 Random Masking and Object Detection 136
4.5.5 Results 137
4.6 Mitotic Cells in the H&E Histopathological Images of Breast Cancer Carcinoma 138
4.6.1 Mitotic Index Estimation 139
4.6.2 Mitotic Candidate Selection 140
4.6.3 Exclusive Independent Component Analysis (XICA) 140
4.6.4 Classification Using Sparse Representation 143
4.6.5 Training and Testing Over Channels 144
4.6.6 Results 146
4.7 Conclusions 147 References 147 PARTIII REGISTRATION AND MATCHING 153 5 3D Nonrigid Image Registration by Parzen-Window-Based Normalized Mutual Information and its Application on Mr-Guided Microwave Thermocoagulation of Liver Tumors 155 Rui Xu, Yen-Wei Chen, Shigehiro Morikawa, and Yoshimasa Kurumi
5.1 Introduction 155
5.2 Parzen-Window-Based Normalized Mutual Information 157
5.2.1 Definition of Parzen-Window Method 157
5.2.2 Parzen-Window-Based Estimation of Joint Histogram 158
5.2.3 Normalized Mutual Information and its Derivative 160
5.3 Analysis of Kernel Selection 163
5.3.1 The Designed Kernel 163
5.3.2 Comparison in Theory 167
5.3.3 Comparison by Experiments 170
5.4 Application on MR-Guided Microwave Thermocoagulation of Liver Tumors 174
5.4.1 Introduction of MR-Guided Microwave Thermocoagulation of Liver Tumors 174
5.4.2 Nonrigid Registration by Parzen-Window-Based Mutual Information 175
5.4.3 Evaluation on Phantom Data 177
5.4.4 Evaluation on Clinical Cases 180
5.5 Conclusion 185 Acknowledgements 186 References 187 6 2D/3D Image Registration For Endovascular Abdominal Aortic Aneurysm (AAA) Repair 189 Shun Miao and Rui Liao
6.1 Introduction 189
6.2 Background 190
6.2.1 Image Modalities 190
6.2.2 2D/3D Registration Framework 192
6.2.3 Feature-Based Registration 194
6.2.4 Intensity-Based Registration 196
6.2.5 Number of Imaging Planes 197
6.2.6 2D/3D Registration for Endovascular AAA Repair 198
6.3 Smart Utilization of Two X-Ray Images for Rigid-Body 2D/3D Registration 199
6.3.1 2D/3D Registration: Challenges in EVAR 199
6.3.2 3D Image Processing and DRR Generation 202
6.3.3 2D Image Processing 203
6.3.4 Similarity Measure 205
6.3.5 Optimization 207
6.3.6 Validation 210
6.4 Deformable 2D/3D Registration 211
6.4.1 Problem Formulation 212
6.4.2 Graph-Based Difference Measure 213
6.4.3 Length Preserving Term 215
6.4.4 Smoothness Term 215
6.4.5 Optimization 216
6.4.6 Validation 217
6.5 Visual Check of Patient Movement Using Pelvis Boundary Detection 220
6.6 Discussion and Conclusion 222 References 223 PARTIV OBJECT TRACKING 229 7 Motion Tracking in Medical Images 231 Chuqing Cao, Chao Li, and Ying Sun
7.1 Introduction 232
7.1.1 Point-Based Tracking 233
7.1.2 Silhouette-Based Tracking 233
7.1.3 Kernel-Based Tracking 233
7.2 Background 234
7.2.1 Point-Based Tracking 234
7.2.2 Silhouette-Based Tracking 236
7.2.3 Kernel-Based Tracking 237
7.2.4 Summary 238
7.3 Bayesian Tracking Methods 238
7.3.1 Kalman Filters 239
7.3.2 Particle Filters 240
7.3.3 Summary of Bayesian Tracking Methods 241
7.4 Deformable Models 241
7.4.1 Mathematical Foundations of Deformable Models 241
7.4.2 Energy-Minimizing Deformable Models 242
7.4.3 Probabilistic Deformable Models 244
7.4.4 Summary of Deformable Models 245
7.5 Motion Tracking Based on the Harmonic Phase Algorithm 246
7.5.1 HARP Imaging 246
7.5.2 HARP Tracking 248
7.5.3 Summary 249
7.6 Case Study: Pseudo Ground Truth-Based Nonrigid Registration of MRI for Tracking the Cardiac Motion 250
7.6.1 Data Fidelity Term 251
7.6.2 Spatial Smoothness Constraint 252
7.6.3 Temporal Smoothness Constraint 253
7.6.4 Energy Minimization 254
7.6.5 Preliminary Results 255
7.6.6 Nonrigid Registration of Myocardial Perfusion MRI 255
7.6.7 Experimental Results 259
7.7 Discussion 264
7.8 Conclusion and Near-Future Trends 265 References 267 PARTV CLASSIFICATION 275 8 Blood Smear Analysis, Malaria Infection Detection, and Grading from Blood Cell Images 277 Wei Xiong, Sim-Heng Ong, Joo-Hwee Lim, Jierong Cheng, and Ying Gu
8.1 Introduction 278
8.2 Pattern Classification Techniques 282
8.2.1 Supervised and Nonsupervised Learning 282
8.2.2 Bayesian Decision Theory 283
8.2.3 Clustering 284
8.2.4 Support Vector Machines 286
8.3 GWA Detection 287
8.3.1 Image Analysis 288
8.3.2 Association between the Object Area and the Number of Cells Per Object 289
8.3.3 Clump Splitting 291
8.3.4 Clump Characterization 293
8.3.5 Classification 295
8.4 Dual-Model-Guided Image Segmentation and Recognition 295
8.4.1 Related Work 296
8.4.2 Strategies and Object Fu
PART I INTRODUCTION 1 1 Overview of Biomedical Image Understanding Methods 3 Wei Xiong, Jierong Cheng, Ying Gu, Shimiao Li and Joo Hwee Lim
1.1 Segmentation and Object Detection 5
1.1.1 Methods Based on Image Processing Techniques 6
1.1.2 Methods Using Pattern Recognition and Machine Learning Algorithms 7
1.1.3 Model and Atlas-Based Segmentation 8
1.1.4 Multispectral Segmentation 9
1.1.5 User Interactions in Interactive Segmentation Methods 10
1.1.6 Frontiers of Biomedical Image Segmentation 11
1.2 Registration 11
1.2.1 Taxonomy of Registration Methods 12
1.2.2 Frontiers of Registration for Biomedical Image Understanding 15
1.3 Object Tracking 16
1.3.1 Object Representation 17
1.3.2 Feature Selection for Tracking 18
1.3.3 Object Tracking Technique 19
1.3.4 Frontiers of Object Tracking 19
1.4 Classification 20
1.4.1 Feature Extraction and Feature Selection 21
1.4.2 Classifiers 22
1.4.3 Unsupervised Classification 23
1.4.4 Classifier Combination 24
1.4.5 Frontiers of Pattern Classification for Biomedical Image Understanding 25
1.5 Knowledge-Based Systems 26
1.5.1 Semantic Interpretation and Knowledge-Based Systems 26
1.5.2 Knowledge-Based Vision Systems 27
1.5.3 Knowledge-Based Vision Systems in Biomedical Image Analysis 28
1.5.4 Frontiers of Knowledge-Based Systems 29 References 29 PARTII SEGMENTATION AND OBJECT DETECTION 47 2 Medical Image Segmentation and its Application in Cardiac MRI 49 Dong Wei, Chao Li, and Ying Sun
2.1 Introduction 50
2.2 Background 51
2.2.1 Active Contour Models 51
2.2.2 Parametric and Nonparametric Contour Representation 52
2.2.3 Graph-Based Image Segmentation 53
2.2.4 Summary 54
2.3 Parametric Active Contours - The Snakes 54
2.3.1 The Internal Spline Energy E int 54
2.3.2 The Image-Derived Energy E img 55
2.3.3 The External Control Energy E con 55
2.3.4 Extension of Snakes and Summary of Parametric Active Contours 57
2.4 Geometric Active Contours - The Level Sets 58
2.4.1 Variational Level Set Methods 58
2.4.2 Region-Based Variational Level Set Methods 60
2.4.3 Summary of Level Set Methods 64
2.5 Graph-Based Methods - The Graph Cuts 65
2.5.1 Basic Graph Cuts Formulation 65
2.5.2 Patch-Based Graph Cuts 66
2.5.3 An Example of Graph Cuts 68
2.5.4 Summary of Graph Cut Methods 72
2.6 Case Study: Cardiac Image Segmentation Using A Dual Level Sets Model 73
2.6.1 Introduction 73
2.6.2 Method 74
2.6.3 Experimental Results 79
2.6.4 Conclusion of the Case Study 81
2.7 Conclusion and Near-Future Trends 81 References 83 3 Morphometric Measurements of the Retinal Vasculature in Fundus Images With Vampire 91 Emanuele Trucco, Andrea Giachetti, Lucia Ballerini, Devanjali Relan, Alessandro Cavinato, and Tom Macgillivray
3.1 Introduction 92
3.2 Assessing Vessel Width 93
3.2.1 Previous Work 93
3.2.2 Our Method 94
3.2.3 Results 95
3.2.4 Discussion 96
3.3 Artery or Vein? 98
3.3.1 Previous Work 98
3.3.2 Our Solution 99
3.3.3 Results 101
3.3.4 Discussion 103
3.4 Are My Program''s Measurements Accurate? 104
3.4.1 Discussion 106 References 107 4 Analyzing Cell and Tissue Morphologies Using Pattern Recognition Algorithms 113 Hwee Kuan Lee, Yan Nei Law, Chao-Hui Huang, and Choon Kong Yap
4.1 Introduction, 113
4.2 Texture Segmentation of Endometrial Images Using the Subspace Mumford-Shah Model 115
4.2.1 Subspace Mumford-Shah Segmentation Model 116
4.2.2 Feature Weights 118
4.2.3 Once-and-For-All Approach 119
4.2.4 Results 119
4.3 Spot Clustering for Detection of Mutants in Keratinocytes 120
4.3.1 Image Analysis Framework 123
4.3.2 Results 124
4.4 Cells and Nuclei Detection 124
4.4.1 Model 125
4.4.2 Neural Cells and Breast Cancer Cells Data 127
4.4.3 Performance Evaluation 127
4.4.4 Robustness Study 127
4.4.5 Results 128
4.5 Geometric Regional Graph Spectral Feature 134
4.5.1 Conversion of Image Patches into Region Signatures 134
4.5.2 Comparing Region Signatures 135
4.5.3 Classification of Region Signatures 136
4.5.4 Random Masking and Object Detection 136
4.5.5 Results 137
4.6 Mitotic Cells in the H&E Histopathological Images of Breast Cancer Carcinoma 138
4.6.1 Mitotic Index Estimation 139
4.6.2 Mitotic Candidate Selection 140
4.6.3 Exclusive Independent Component Analysis (XICA) 140
4.6.4 Classification Using Sparse Representation 143
4.6.5 Training and Testing Over Channels 144
4.6.6 Results 146
4.7 Conclusions 147 References 147 PARTIII REGISTRATION AND MATCHING 153 5 3D Nonrigid Image Registration by Parzen-Window-Based Normalized Mutual Information and its Application on Mr-Guided Microwave Thermocoagulation of Liver Tumors 155 Rui Xu, Yen-Wei Chen, Shigehiro Morikawa, and Yoshimasa Kurumi
5.1 Introduction 155
5.2 Parzen-Window-Based Normalized Mutual Information 157
5.2.1 Definition of Parzen-Window Method 157
5.2.2 Parzen-Window-Based Estimation of Joint Histogram 158
5.2.3 Normalized Mutual Information and its Derivative 160
5.3 Analysis of Kernel Selection 163
5.3.1 The Designed Kernel 163
5.3.2 Comparison in Theory 167
5.3.3 Comparison by Experiments 170
5.4 Application on MR-Guided Microwave Thermocoagulation of Liver Tumors 174
5.4.1 Introduction of MR-Guided Microwave Thermocoagulation of Liver Tumors 174
5.4.2 Nonrigid Registration by Parzen-Window-Based Mutual Information 175
5.4.3 Evaluation on Phantom Data 177
5.4.4 Evaluation on Clinical Cases 180
5.5 Conclusion 185 Acknowledgements 186 References 187 6 2D/3D Image Registration For Endovascular Abdominal Aortic Aneurysm (AAA) Repair 189 Shun Miao and Rui Liao
6.1 Introduction 189
6.2 Background 190
6.2.1 Image Modalities 190
6.2.2 2D/3D Registration Framework 192
6.2.3 Feature-Based Registration 194
6.2.4 Intensity-Based Registration 196
6.2.5 Number of Imaging Planes 197
6.2.6 2D/3D Registration for Endovascular AAA Repair 198
6.3 Smart Utilization of Two X-Ray Images for Rigid-Body 2D/3D Registration 199
6.3.1 2D/3D Registration: Challenges in EVAR 199
6.3.2 3D Image Processing and DRR Generation 202
6.3.3 2D Image Processing 203
6.3.4 Similarity Measure 205
6.3.5 Optimization 207
6.3.6 Validation 210
6.4 Deformable 2D/3D Registration 211
6.4.1 Problem Formulation 212
6.4.2 Graph-Based Difference Measure 213
6.4.3 Length Preserving Term 215
6.4.4 Smoothness Term 215
6.4.5 Optimization 216
6.4.6 Validation 217
6.5 Visual Check of Patient Movement Using Pelvis Boundary Detection 220
6.6 Discussion and Conclusion 222 References 223 PARTIV OBJECT TRACKING 229 7 Motion Tracking in Medical Images 231 Chuqing Cao, Chao Li, and Ying Sun
7.1 Introduction 232
7.1.1 Point-Based Tracking 233
7.1.2 Silhouette-Based Tracking 233
7.1.3 Kernel-Based Tracking 233
7.2 Background 234
7.2.1 Point-Based Tracking 234
7.2.2 Silhouette-Based Tracking 236
7.2.3 Kernel-Based Tracking 237
7.2.4 Summary 238
7.3 Bayesian Tracking Methods 238
7.3.1 Kalman Filters 239
7.3.2 Particle Filters 240
7.3.3 Summary of Bayesian Tracking Methods 241
7.4 Deformable Models 241
7.4.1 Mathematical Foundations of Deformable Models 241
7.4.2 Energy-Minimizing Deformable Models 242
7.4.3 Probabilistic Deformable Models 244
7.4.4 Summary of Deformable Models 245
7.5 Motion Tracking Based on the Harmonic Phase Algorithm 246
7.5.1 HARP Imaging 246
7.5.2 HARP Tracking 248
7.5.3 Summary 249
7.6 Case Study: Pseudo Ground Truth-Based Nonrigid Registration of MRI for Tracking the Cardiac Motion 250
7.6.1 Data Fidelity Term 251
7.6.2 Spatial Smoothness Constraint 252
7.6.3 Temporal Smoothness Constraint 253
7.6.4 Energy Minimization 254
7.6.5 Preliminary Results 255
7.6.6 Nonrigid Registration of Myocardial Perfusion MRI 255
7.6.7 Experimental Results 259
7.7 Discussion 264
7.8 Conclusion and Near-Future Trends 265 References 267 PARTV CLASSIFICATION 275 8 Blood Smear Analysis, Malaria Infection Detection, and Grading from Blood Cell Images 277 Wei Xiong, Sim-Heng Ong, Joo-Hwee Lim, Jierong Cheng, and Ying Gu
8.1 Introduction 278
8.2 Pattern Classification Techniques 282
8.2.1 Supervised and Nonsupervised Learning 282
8.2.2 Bayesian Decision Theory 283
8.2.3 Clustering 284
8.2.4 Support Vector Machines 286
8.3 GWA Detection 287
8.3.1 Image Analysis 288
8.3.2 Association between the Object Area and the Number of Cells Per Object 289
8.3.3 Clump Splitting 291
8.3.4 Clump Characterization 293
8.3.5 Classification 295
8.4 Dual-Model-Guided Image Segmentation and Recognition 295
8.4.1 Related Work 296
8.4.2 Strategies and Object Fu
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