Handbook of Biomedical Image Analysis (eBook)

Jasjit Suri, Ph.D. has spent the last 20 years in the field of computer and electrical engineering, and more than a decade in imaging sciences. Dr. Suri has a masters in computer sciences from the University of Illinois, a doctorate from the University of Washington, Seattle, and will soon receive his EMBA from the Weatherhead School of Management at Case Western Reserve University, Cleveland, Ohio. Dr. Suri has published over 100 technical publications in medical imaging, is a senior member of IEEE, member of the engineering honor societies Eta-Kappa-Nu and Tau-Beta-Phi, and a recipient of the President's Gold Medal in 1980. Prof. Swamy Laxminarayan, D.Sci. championed the field of Biomedical Engineering for over 30 years having held a variety of senior positions within the industry. He is an internationally recognized scientist, engineer, and educator with over 200 technical publications in biomedical information technology, computation biology, signal and image processing, biotechnology, and physiological system modeling. Prof. Laxminarayan is a fellow of AIMBE and a recipient of IEEE 3rd Millennium Medal.

Contributors 7
Acknowledgments 9
Preface 11
Contents 17
Chapter 1 A Basic Model for IVUS Image Simulation 19
Introduction 19
1.1 Technical Aspects of the IVUS 21
1.2 Present Limitations of the IVUS Technique and the Need for a Generation Model of IVUS Data 23
1.3 Formal Definition of the Image Model 27
1.4 Principal Features of IVUS Data 30
1.5 Simulation of IVUS Image 41
1.6 Validation of the Image Simulation Model 47
1.7 Conclusions 69
Questions 70
Bibliography 71
Chapter 2 Quantitative Functional Imaging with Positron Emission Tomography: Principles and Instrumentation 74
2.1 Introduction 74
2.2 A Brief History of PET 76
2.3 Modes of Decay 78
2.4 Positron Annihilation 79
2.5 Coincidence Detection 80
2.6 Coincidence Criteria 82
2.7 Detectors 83
2.8 Detected Events 84
2.9 Data Acquisition 86
2.10 Image Reconstruction 88
2.11 Data Corrections 93
2.12 Calibration 100
2.13 Resolution Limitations of PET 100
2.14 Quantitative Physiological Parameter Estimation 103
2.15 Applications of PET 112
2.16 Discussion and Concluding Remarks 115
Acknowledgment 119
Questions 119
Bibliography 121
Chapter 3 Advances in Magnetic Resonance Angiography and Physical Principles 134
3.1 Introduction 134
3.2 Techniques and Principles of Magnetic Resonance Angiography 151
3.3 Acquisition Methods 186
3.4 Recent Advancement in MRA Techniques 189
3.5 Limitations and Future Prospects 210
Questions 211
Bibliography 212
Chapter 4 Recent Advances in the Level Set Method 217
4.1 Introduction 217
4.2 Basic Level Set Method 219
4.3 Recent Developments 238
4.4 Conclusion 258
Questions 259
Bibliography 260
Chapter 5 Shape From Shading Models 273
5.1 Introduction 273
5.2 Mathematical Background of SFS Models 277
5.3 Numerical Algorithms and Their Implementations 288
5.4 Wavelet-Based Methods 304
5.5 Concluding Remarks 313
5.6 Acknowledgements 314
Bibliography 315
Chapter 6 Wavelets in Medical Image Processing: Denoising, Segmentation, and Registration 321
6.1 Introduction 321
6.2 Wavelet Transform and Multiscale Analysis 322
6.3 Noise Reduction and Image Enhancement Using Wavelet Transforms 335
6.4 Image Segmentation Using Wavelets 354
6.5 Image Registration Using Wavelets 359
6.6 Summary 360
Questions 363
Bibliography 364
Chapter 7 Improving the Initialization, Convergence, and Memory Utilization for Deformable Models 375
7.1 Introduction 375
7.2 Background in Deformable Models 380
7.3 Initializing Traditional Deformable Models 387
7.4 Initialization of T-Surfaces 393
7.5 Reconstruction Method 397
7.6 Out-of-Core for Improving Memory Utilization 399
7.7 Out-of-Core Segmentation Approach 402
7.8 Convergence of Deformable Models and Diffusion Methods 404
7.9 Experimental Results 409
7.10 Discussion and Perspectives 415
7.11 Conclusions 419
Questions 420
Bibliography 423
Chapter 8 Level Set Segmentation of Biological Volume Datasets 431
8.1 Introduction 431
8.2 Level Set Surface Models 433
8.3 Segmentation Framework 436
8.4 Segmentation From Multiple Nonuniform Volume Datasets 445
8.5 Segmentation of DT-MRI Brain Data 456
8.6 Direct Estimation of Surfaces in Tomographic Data 466
8.7 Conclusions 482
8.8 Acknowledgements 484
Bibliography 485
Chapter 9 Advanced Segmentation Techniques 495
9.1 Introduction 495
9.2 Stochastic Image Models 497
9.3 Applications 506
9.4 Fuzzy Segmentation 514
9.5 Level Sets 528
9.6 Application: MRA Data Segmentation Using Level Sets 536
Questions 541
Bibliography 543
Chapter 10 A Region- Aided Color Geometric Snake 550
10.1 Introduction 550
10.2 The Geometric Snake 552
10.3 The Geometric GGVF Snake 559
10.4 Region-Aided Geometric Snake 561
10.5 Numerical Solutions 567
10.6 Region-Aided Geometric Snake on Vector- Valued Images 570
10.7 The Mean Shift Algorithm 572
10.8 A Summary of the RAGS Algorithm 574
10.9 Experiments and Results 574
10.10 Conclusions 586
10.11 Further Reading 587
10.12 Appendix. Numerical Solution for the Level Set Implementation of RAGS 589
Questions 590
Bibliography 592
Chapter 11 Co- Volume Level Set Method in Subjective Surface Based Medical Image Segmentation 597
11.1 Introduction 597
11.2 Discussion on Related Mathematical Models 601
11.3 Semi-implicit Co-Volume Scheme 612
11.4 Discussion on Numerical Results 623
11.5 Conclusions 633
11.6 Acknowledgements 634
Questions 634
Bibliography 635
The Editors 641
Index 646