Microscopic image analysis for life science applications:
Saved in:
| Format: | Book |
|---|---|
| Language: | English |
| Published: |
London [u.a.]
Artech House
2008
|
| Series: | Bioinformatics & biomedical imaging
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXII, 489 S. Ill., graph. Darst. 1 CD-ROM (12 cm) |
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MARC
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| 245 | 1 | 0 | |a Microscopic image analysis for life science applications |c Jens Rittscher, Stephen T. C. Wong, and Raghu Machiraju, ed. |
| 264 | 1 | |a London [u.a.] |b Artech House |c 2008 | |
| 300 | |a XXII, 489 S. |b Ill., graph. Darst. |e 1 CD-ROM (12 cm) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Bioinformatics & biomedical imaging | |
| 650 | 4 | |a Image Enhancement | |
| 650 | 4 | |a Image Interpretation, Computer-Assisted |x methods | |
| 650 | 4 | |a Image analysis | |
| 650 | 4 | |a Imaging systems | |
| 650 | 4 | |a Imaging, Three-Dimensional | |
| 650 | 4 | |a Microscopy | |
| 650 | 4 | |a Microscopy |x methods | |
| 650 | 4 | |a Tissue Array Analysis |x methods | |
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| 700 | 1 | |a Rittscher, Jens |e Sonstige |4 oth | |
| 700 | 1 | |a Wong, Stephen T. C. |e Sonstige |4 oth | |
| 700 | 1 | |a Machiraju, Raghu |e Sonstige |4 oth | |
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Record in the Search Index
| _version_ | 1804137772810764288 |
|---|---|
| adam_text | Contents
Foreword xv
Preface xvii
Introduction
Introduction to Biological Light Microscopy 1
1.1 Introduction 1
1.2 Need for Microscopy 1
1.3 Image Formation in Transmitted Light Microscopy 2
1.4 Resolution, Magnification, and Contrast in Microscopy 5
1.5 Phase Contrast Microscopy 7
1.6 Dark Field Microscopy 8
1.7 Polarization Microscopy 8
1.8 Differential Interference Contrast Microscopy 9
1.9 Reflected Light Microscopy 10
1.10 Fluorescence Microscopy 12
1.11 Light Microscopy in Biology 12
1.12 Noise and Artifacts in Microscopic Images 14
1.13 Trends in Light Microscopy 16
References 17
Molecular Probes for Fluorescence Microscopy 19
2.1 Introduction 19
2.2 Basic Characteristics of Fluorophores 21
2.3 Traditional Fluorescent Dyes 25
2.4 Alexa Fluor Dyes 26
2.5 Cyanine Dyes 28
2.6 Fluorescent Environmental Probes 29
2.7 Organelle Probes 32
2.8 Quantum Dots 34
2.9 Fluorescent Proteins 36
2.10 Hybrid Systems 39
2.11 Quenching and Photobleaching 40
2.12 Conclusions 43
References 43
Selected Bibliography 48
v
w Contents
Overview of Image Analysis Tools and Tasks for Microscopy 49
3.1 Image Analysis Framework 50
3.1.1 Continuous-Domain Image Processing 51
3.1.2 A/D Conversion 54
3.1.3 Discrete-Domain Image Processing 55
3.2 Image Analysis Tools for Microscopy 56
3.2.1 Signal and Image Representations 57
3.2.2 Fourier Analysis 60
3.2.3 Gabor Analysis 61
3.2.4 Multiresolution Analysis 62
3.2.5 Unsupervised, Data-Driven Representation and
Analysis Methods 64
3.2.6 Statistical Estimation 67
3.3 Imaging Tasks in Microscopy 68
3.3.1 Intelligent Acquisition 68
3.3.2 Deconvolution, Denoising, and Restoration 69
3.3.3 Registration and Mosaicking 72
3.3.4 Segmentation, Tracing, and Tracking 74
3.3.5 Classification and Clustering 77
3.3.6 Modeling 78
3.4 Conclusions 79
References 79
An Introduction to Fluorescence Microscopy: Basic Principles,
Challenges, and Opportunities 85
4.1 Fluorescence in Molecular and Cellular Biology 86
4.1.1 The Physical Principles of Fluorescence 86
4.1.2 The Green Revolution 88
4.2 Microscopes and Image Formation 91
4.2.1 The Widefield Microscope 91
4.2.2 The Confocal Scanning Microscope 93
4.2.3 Sample Setup and Aberrations 94
4.3 Detectors 95
4.3.1 Characteristic Parameters of Detection Systems 95
4.3.2 Detection Technologies 96
4.4 Limiting Factors of Fluorescence Imaging 98
4.4.1 Noise Sources 98
4.4.2 Sample-Dependent Limitations 99
4.5 Advanced Experimental Techniques 99
4.5.1 FRET 100
4.5.2 FRAP 101
4.5.3 FLIM 102
Contents vi[
4.6 Signal and Image Processing Challenges 103
4.6.1 Data Size and Dimensionality 103
4.6.2 Image Preparation 103
4.6.3 Restoration 104
4.6.4 Registration 105
4.6.5 Segmentation 106
4.6.6 Quantitative Analysis 106
4.7 Current and Future Trends 107
4.7.1 Fluorescent Labels 107
4.7.2 Advanced Microscopy Systems 108
4.7.3 Super-Resolution: Photoactivated Localization-
Based Techniques 109
4.8 Conclusions 110
References 111
FARSIGHT: A Divide and Conquer Methodology for Analyzing
Complex and Dynamic Biological Microenvironments 115
5.1 Introduction 115
5.2 A Divide-and-Conquer Segmentation Strategy 122
5.3 Computing and Representing Image-Based Measurements 131
5.4 Analysis of Spatio-Temporal Associations 135
5.5 Validation of Automated Image Analysis Results 142
5.6 Summary, Discussion, and Future Directions 145
References 146
Subcellular Structures and Events
MST-Cut: A Minimum Spanning Tree-Based Image Mining Tool and
Its Applications in Automatic Clustering of Fruit Fly Embryonic Gene
Expression Patterns and Predicting Regulatory Motifs 153
6.1 Introduction 153
6.2 MST 154
6.3 MST-Cut for Clustering of Coexpressed/Coregulated Genes 154
6.3.1 MST-Cut Clustering Algorithm 155
6.3.2 Embryonic Image Clustering 157
6.4 Experiments 160
6.4.1 Performance of MST-Cut on Synthetic Datasets 160
6.4.2 Detection of Coregulated Genes and Regulatory
Motifs 163
6.5 Conclusions 166
References 167
Selected Bibliography 168
vjjl Contents
Simulation and Estimation of Intracellular Dynamics and Trafficking 169
7.1 Context 169
7.1.1 Introduction to Intracellular Traffic 170
7.1.2 Introduction to Living Cell Microscopy 171
7.2 Modeling and Simulation Framework 172
7.2.1 Intracellular Trafficking Models in
Video-Microscopy 172
7.2.2 Intracellular Traffic Simulation 174
7.2.3 Example 177
7.3 Background Estimation in Video-Microscopy 178
7.3.1 Pixel-Wise Estimation 178
7.3.2 Spatial Coherence for Background Estimation 179
7.3.3 Example 181
7.4 Foreground Analysis: Network Tomography 181
7.4.1 Network Tomography Principle 182
7.4.2 Measurements 184
7.4.3 Problem Optimization 185
7.4.4 Experiments 187
7.5 Conclusions 187
References 188
Techniques for Cellular and Tissue-Based Image Quantitation of
Protein Biomarkers 191
8.1 Current Methods for Histological and Tissue-Based
Biomarker 191
8.2 Multiplexing 192
8.2.1 Fluorescence Microscopy 192
8.2.2 Fluorescent Dyes 193
8.2.3 Quantum Dots 193
8.2.4 Photobleaching 194
8.3 Image Analysis 194
8.3.1 Image Preprocessing 195
8.3.2 Image Registration 197
8.3.3 Image Segmentation 199
8.3.4 A Unified Segmentation Algorithm 200
8.3.5 Segmentation of Cytoplasm and Epithelial
Regions 202
8.4 Multichannel Segmentation Techniques 202
8.5 Quantitation of Subcellular Biomarkers 203
8.6 Summary 204
References 204
Contents /x
Methods for High-Content, High-Throughput, Image-Based
Cell Screening 209
9.1 Introduction 209
9.2 Challenges in Image-Based High-Content Screening 210
9.3 Methods 210
9.3.1 Illumination and Staining Correction 210
9.3.2 Segmentation 212
9.3.3 Measurements 214
9.3.4 Spatial Bias Correction 214
9.3.5 Exploration and Inference 215
9.4 Discussion 218
References 219
Structure and Dynamics of Cell Populations
Particle Tracking in 3D+t Biological Imaging 223
10.1 Introduction 223
10.2 Main Tracking Methods 225
10.2.1 Autocorrelation Methods 225
10.2.2 Deterministic Methods 226
10.2.3 Multiple Particle Tracking Methods 226
10.2.4 Bayesian Methods 227
10.3 Analysis of Bayesian Filters 229
10.3.1 The Conceptual Filter 229
10.3.2 The Kalman Filter 231
10.3.3 The Filter Based on a Grid 234
10.3.4 The Extended Kalman Filter 235
10.3.5 The Interacting Multiple Model Filter 238
10.3.6 The Approximated Filter Based on a Grid 242
10.3.7 The Particle Filter 245
10.4 Description of the Main Association Methods 250
10.4.1 The Nearest Neighbor (ML) 252
10.4.2 Multihypothesis Tracking (MHT) 254
10.4.3 The Probabilistic Data Association Filter (PDAF) 255
10.4.4 Joint PDAF (JPDAF) 257
10.5 Particle Tracking: Methods for Biological Imaging 258
10.5.1 Proposed Dynamic Models for the IMM 259
10.5.2 Adaptive Validation Gate 261
10.5.3 Association 264
10.6 Applications 265
10.6.1 Validation on Synthetic Data 265
x Contents
10.6.2 Applications to Cell Biology 267
10.7 Conclusions 269
References 270
Appendix 10A Pseudocodes for the Algorithms 276
Automated Analysis of the Mitotic Phases of Human Cells
in 3-D Fluorescence Microscopy Image Sequences 283
11.1 Introduction 283
11.2 Methods 284
11.2.1 Image Analysis Workflow 284
11.2.2 Segmentation of Multicell Images 285
11.2.3 Tracking of Mitotic Cell Nuclei 287
11.2.4 Extraction of Static and Dynamic Features 288
11.2.5 Classification 289
11.3 Experimental Results 290
11.3.1 Image Data 290
11.3.2 Classification Results 290
11.4 Discussion and Conclusion 292
References 292
Automated Spatio-Temporal Cell Cycle Phase Analysis Based on
Covert GFP Sensors 295
12.1 Introduction 295
12.2 Biological Background 296
12.2.1 Cell Cycle Phases 296
12.2.2 Cell Cycle Checkpoints 297
12.2.3 Cell Staining 298
12.2.4 Problem Statement 299
12.3 State of the Art 300
12.4 Mathematical Framework: Level Sets 302
12A.I Active Contours with Edges 303
12.4.2 Active Contours Without Edges 303
12.5 Spatio-Temporal Cell Cycle Phase Analysis 304
12.5.1 Automatic Seed Placement 305
12.5.2 Shape/Size Constraint for Level Set Segmentation 305
12.5.3 Model-Based Fast Marching Cell Phase Tracking 307
12.6 Results 310
12.6.1 Large-Scale Toxicological Study 310
12.6.2 Algorithmic Validation 310
12.7 A Tool for Cell Cycle Research 311
12.7.1 Research Prototype 312
12.7.2 Visualization 313
12.8 Summary and Future Work 314
References 314
Contents x[
Cell Segmentation for Division Rate Estimation in Computerized
Video Time-Lapse Microscopy 317
13.1 Introduction 317
13.2 Methodology 319
13.2.1 Cell Detection with AdaBoost 319
13.2.2 Foreground Segmentation 323
13.2.3 Cytoplasm Segmentation Using the Watershed
Algorithm 326
13.2.4 Cell Division Rate Estimation 327
13.3 Experiments 328
13.4 Conclusions 329
References 329
Automated Cellular and Tissue Analysis
Systems Biology and the Digital Fish Project: A Vast New Frontier
for Image Analysis 331
14.1 Introduction 331
14.2 Imaging-Based Systems Biology 331
14.2.1 What Is Systems Biology? 331
14.2.2 Imaging in Systems Biology 335
14.3 Example: The Digital Fish Project 338
14.3.1 Goals of Project 338
14.3.2 Why Fish? 340
14.3.3 Imaging 340
14.3.4 Image Analysis 347
14.3.5 Visualization 352
14.3.6 Data Analysis 352
14.3.7 Registration/Integration, Reference Atlas 357
14.4 Bridging the Gap 353
14.4.1 Open Source 353
14.4.2 Traversing the Gap 354
14.5 Conclusions 354
References 355
Quantitative Phenotyping Using Microscopic Images 357
15.1 Introduction 357
15.2 Relevant Biomedical Applications 359
xjl Contents
15.2.1 Mouse Model Phenotyping Study: Role of the
Rb Gene 359
15.2.2 Mouse Model Phenotyping Study: The PTEN
Gene and Cancer 359
15.2.3 3-D Reconstruction of Cellular Structure of
Zebrafish Embryo 360
15.3 Tissue Segmentation Using N-Point Correlation Functions 361
15.3.1 Introduction to N-Point Correlation Functions 361
15.3.2 Segmentation of Microscopic Images Using N-pcfs 364
15.4 Segmentation of Individual Cells 364
15.4.1 Modality-Dependent Segmentation: Active
Contour Models 364
15.4.2 Modality-Independent Segmentation: Using
Tessellations 366
15.5 Registration of Large Microscopic Images 371
15.5.1 Rigid Registration 371
15.5.2 Nonrigid Registration 372
15.6 3-D Visualization 375
15.6.1 Mouse Model Phenotyping Study: Role of the
Rb Gene 375
15.6.2 Mouse Model Phenotyping Study: Role of the
PTEN Gene in Cancer 377
15.6.3 Zebrafish Phenotyping Studies 379
15.7 Quantitative Validation 380
15.7.1 Mouse Placenta Phenotyping Studies 380
15.7.2 Mouse Mammary Gland Phenotyping Study 382
15.8 Summary 384
References 389
Automatic 3-D Morphological Reconstruction of Neuron Cells from
Multiphoton Images 389
16.1 Introduction 389
16.2 Materials and Methods 391
16.2.1 Experimental Data 391
16.3 Results 396
16.4 Conclusions 397
References 398
Robust 3-D Reconstruction and Identification of Dendritic Spines 401
17.1 Introduction 401
17.2 Related Work 404
17.3 Image Acquisition and Processing 404
17.3.1 Data-Set 405
17.3.2 Denoising and Resampling 405
Contents xii[
17.3.3 Segmenting the Neuron 407
17.3.4 Floating Spine Heads 408
17.4 Neuron Reconstruction and Analysis 409
17A.I Surfacing and Surface Fairing 410
17.4.2 Curve Skeletonization 412
17.4.3 Dendrite Tree Model 413
17.4.4 Morphometry and Spine Identification 413
17.5 Results 414
17.6 Conclusion 419
References 419
In Vivo Microscopy
Small Critter Imaging 425
18.1 In Vivo Molecular Small Animal Imaging 425
18.1.1 Fluorescence Microscopic Imaging 425
18.1.2 Bioluminescence Imaging 426
18.1.3 Coherent Anti-Stokes Raman Scattering Imaging 426
18.1.4 Fibered In Vivo Imaging 427
18.2 Fluorescence Molecular Imaging (FMT) 427
18.2.1 Fluorescence Scanning 427
18.2.2 FMT Data Processing 428
18.2.3 Multimodality 428
18.3 Registration of 3-D FMT and MicroCT Images 429
18.3.1 Introduction 429
18.3.2 Problem Statement and Formulation 430
18.3.3 Combined Differential Evolution and Simplex
Method Optimization 432
18.3.4 A Novel Optimization Method Based
on Sequential Monte Carlo 436
18.4 Conclusions 438
References 439
Processing of In Vivo Fibered Confocal Microscopy Video Sequences 441
19.1 Motivations 441
19.2 Principles of Fibered Confocal Microscopy 443
19.2.1 Confocal Microscopy 443
19.2.2 Distal Scanning Fibered Confocal Microscopy 443
19.2.3 Proximal Scanning Fibered Confocal Microscopy 444
19.3 Real-Time Fiber Pattern Rejection 447
19.3.1 Calibrated Raw Data Acquisition 447
xiv Contents
19.3.2 Real-Time Processing 448
19.4 Blood Flow Velocimetry Using Motion Artifacts 450
19.4.1 Imaging of Moving Objects 450
19.4.2 Velocimetry Algorithm 451
19.4.3 Results and Evaluation 453
19.5 Region Tracking for Kinetic Analysis 454
19.5.1 Motion Compensation Algorithm 454
19.5.2 Affine Registration Algorithm 455
19.5.3 Application to Cell Trafficking 456
19.6 Mosaicking: Bridging the Gap Between Microscopic and
Macroscopic Scales 457
19.6.1 Overview of the Algorithm 458
19.6.2 Results and Evaluation 460
19.7 Conclusions 461
References 461
About the Editors 465
List of Contributors 467
Index 473
|
| adam_txt |
Contents
Foreword xv
Preface xvii
Introduction
Introduction to Biological Light Microscopy 1
1.1 Introduction 1
1.2 Need for Microscopy 1
1.3 Image Formation in Transmitted Light Microscopy 2
1.4 Resolution, Magnification, and Contrast in Microscopy 5
1.5 Phase Contrast Microscopy 7
1.6 Dark Field Microscopy 8
1.7 Polarization Microscopy 8
1.8 Differential Interference Contrast Microscopy 9
1.9 Reflected Light Microscopy 10
1.10 Fluorescence Microscopy 12
1.11 Light Microscopy in Biology 12
1.12 Noise and Artifacts in Microscopic Images 14
1.13 Trends in Light Microscopy 16
References 17
Molecular Probes for Fluorescence Microscopy 19
2.1 Introduction 19
2.2 Basic Characteristics of Fluorophores 21
2.3 Traditional Fluorescent Dyes 25
2.4 Alexa Fluor Dyes 26
2.5 Cyanine Dyes 28
2.6 Fluorescent Environmental Probes 29
2.7 Organelle Probes 32
2.8 Quantum Dots 34
2.9 Fluorescent Proteins 36
2.10 Hybrid Systems 39
2.11 Quenching and Photobleaching 40
2.12 Conclusions 43
References 43
Selected Bibliography 48
v
w Contents
Overview of Image Analysis Tools and Tasks for Microscopy 49
3.1 Image Analysis Framework 50
3.1.1 Continuous-Domain Image Processing 51
3.1.2 A/D Conversion 54
3.1.3 Discrete-Domain Image Processing 55
3.2 Image Analysis Tools for Microscopy 56
3.2.1 Signal and Image Representations 57
3.2.2 Fourier Analysis 60
3.2.3 Gabor Analysis 61
3.2.4 Multiresolution Analysis 62
3.2.5 Unsupervised, Data-Driven Representation and
Analysis Methods 64
3.2.6 Statistical Estimation 67
3.3 Imaging Tasks in Microscopy 68
3.3.1 Intelligent Acquisition 68
3.3.2 Deconvolution, Denoising, and Restoration 69
3.3.3 Registration and Mosaicking 72
3.3.4 Segmentation, Tracing, and Tracking 74
3.3.5 Classification and Clustering 77
3.3.6 Modeling 78
3.4 Conclusions 79
References 79
An Introduction to Fluorescence Microscopy: Basic Principles,
Challenges, and Opportunities 85
4.1 Fluorescence in Molecular and Cellular Biology 86
4.1.1 The Physical Principles of Fluorescence 86
4.1.2 The Green Revolution 88
4.2 Microscopes and Image Formation 91
4.2.1 The Widefield Microscope 91
4.2.2 The Confocal Scanning Microscope 93
4.2.3 Sample Setup and Aberrations 94
4.3 Detectors 95
4.3.1 Characteristic Parameters of Detection Systems 95
4.3.2 Detection Technologies 96
4.4 Limiting Factors of Fluorescence Imaging 98
4.4.1 Noise Sources 98
4.4.2 Sample-Dependent Limitations 99
4.5 Advanced Experimental Techniques 99
4.5.1 FRET 100
4.5.2 FRAP 101
4.5.3 FLIM 102
Contents vi[
4.6 Signal and Image Processing Challenges 103
4.6.1 Data Size and Dimensionality 103
4.6.2 Image Preparation 103
4.6.3 Restoration 104
4.6.4 Registration 105
4.6.5 Segmentation 106
4.6.6 Quantitative Analysis 106
4.7 Current and Future Trends 107
4.7.1 Fluorescent Labels 107
4.7.2 Advanced Microscopy Systems 108
4.7.3 Super-Resolution: Photoactivated Localization-
Based Techniques 109
4.8 Conclusions 110
References 111
FARSIGHT: A Divide and Conquer Methodology for Analyzing
Complex and Dynamic Biological Microenvironments 115
5.1 Introduction 115
5.2 A Divide-and-Conquer Segmentation Strategy 122
5.3 Computing and Representing Image-Based Measurements 131
5.4 Analysis of Spatio-Temporal Associations 135
5.5 Validation of Automated Image Analysis Results 142
5.6 Summary, Discussion, and Future Directions 145
References 146
Subcellular Structures and Events
MST-Cut: A Minimum Spanning Tree-Based Image Mining Tool and
Its Applications in Automatic Clustering of Fruit Fly Embryonic Gene
Expression Patterns and Predicting Regulatory Motifs 153
6.1 Introduction 153
6.2 MST 154
6.3 MST-Cut for Clustering of Coexpressed/Coregulated Genes 154
6.3.1 MST-Cut Clustering Algorithm 155
6.3.2 Embryonic Image Clustering 157
6.4 Experiments 160
6.4.1 Performance of MST-Cut on Synthetic Datasets 160
6.4.2 Detection of Coregulated Genes and Regulatory
Motifs 163
6.5 Conclusions 166
References 167
Selected Bibliography 168
vjjl Contents
Simulation and Estimation of Intracellular Dynamics and Trafficking 169
7.1 Context 169
7.1.1 Introduction to Intracellular Traffic 170
7.1.2 Introduction to Living Cell Microscopy 171
7.2 Modeling and Simulation Framework 172
7.2.1 Intracellular Trafficking Models in
Video-Microscopy 172
7.2.2 Intracellular Traffic Simulation 174
7.2.3 Example 177
7.3 Background Estimation in Video-Microscopy 178
7.3.1 Pixel-Wise Estimation 178
7.3.2 Spatial Coherence for Background Estimation 179
7.3.3 Example 181
7.4 Foreground Analysis: Network Tomography 181
7.4.1 Network Tomography Principle 182
7.4.2 Measurements 184
7.4.3 Problem Optimization 185
7.4.4 Experiments 187
7.5 Conclusions 187
References 188
Techniques for Cellular and Tissue-Based Image Quantitation of
Protein Biomarkers 191
8.1 Current Methods for Histological and Tissue-Based
Biomarker 191
8.2 Multiplexing 192
8.2.1 Fluorescence Microscopy 192
8.2.2 Fluorescent Dyes 193
8.2.3 Quantum Dots 193
8.2.4 Photobleaching 194
8.3 Image Analysis 194
8.3.1 Image Preprocessing 195
8.3.2 Image Registration 197
8.3.3 Image Segmentation 199
8.3.4 A Unified Segmentation Algorithm 200
8.3.5 Segmentation of Cytoplasm and Epithelial
Regions 202
8.4 Multichannel Segmentation Techniques 202
8.5 Quantitation of Subcellular Biomarkers 203
8.6 Summary 204
References 204
Contents /x
Methods for High-Content, High-Throughput, Image-Based
Cell Screening 209
9.1 Introduction 209
9.2 Challenges in Image-Based High-Content Screening 210
9.3 Methods 210
9.3.1 Illumination and Staining Correction 210
9.3.2 Segmentation 212
9.3.3 Measurements 214
9.3.4 Spatial Bias Correction 214
9.3.5 Exploration and Inference 215
9.4 Discussion 218
References 219
Structure and Dynamics of Cell Populations
Particle Tracking in 3D+t Biological Imaging 223
10.1 Introduction 223
10.2 Main Tracking Methods 225
10.2.1 Autocorrelation Methods 225
10.2.2 Deterministic Methods 226
10.2.3 Multiple Particle Tracking Methods 226
10.2.4 Bayesian Methods 227
10.3 Analysis of Bayesian Filters 229
10.3.1 The Conceptual Filter 229
10.3.2 The Kalman Filter 231
10.3.3 The Filter Based on a Grid 234
10.3.4 The Extended Kalman Filter 235
10.3.5 The Interacting Multiple Model Filter 238
10.3.6 The Approximated Filter Based on a Grid 242
10.3.7 The Particle Filter 245
10.4 Description of the Main Association Methods 250
10.4.1 The Nearest Neighbor (ML) 252
10.4.2 Multihypothesis Tracking (MHT) 254
10.4.3 The Probabilistic Data Association Filter (PDAF) 255
10.4.4 Joint PDAF (JPDAF) 257
10.5 Particle Tracking: Methods for Biological Imaging 258
10.5.1 Proposed Dynamic Models for the IMM 259
10.5.2 Adaptive Validation Gate 261
10.5.3 Association 264
10.6 Applications 265
10.6.1 Validation on Synthetic Data 265
x Contents
10.6.2 Applications to Cell Biology 267
10.7 Conclusions 269
References 270
Appendix 10A Pseudocodes for the Algorithms 276
Automated Analysis of the Mitotic Phases of Human Cells
in 3-D Fluorescence Microscopy Image Sequences 283
11.1 Introduction 283
11.2 Methods 284
11.2.1 Image Analysis Workflow 284
11.2.2 Segmentation of Multicell Images 285
11.2.3 Tracking of Mitotic Cell Nuclei 287
11.2.4 Extraction of Static and Dynamic Features 288
11.2.5 Classification 289
11.3 Experimental Results 290
11.3.1 Image Data 290
11.3.2 Classification Results 290
11.4 Discussion and Conclusion 292
References 292
Automated Spatio-Temporal Cell Cycle Phase Analysis Based on
Covert GFP Sensors 295
12.1 Introduction 295
12.2 Biological Background 296
12.2.1 Cell Cycle Phases 296
12.2.2 Cell Cycle Checkpoints 297
12.2.3 Cell Staining 298
12.2.4 Problem Statement 299
12.3 State of the Art 300
12.4 Mathematical Framework: Level Sets 302
12A.I Active Contours with Edges 303
12.4.2 Active Contours Without Edges 303
12.5 Spatio-Temporal Cell Cycle Phase Analysis 304
12.5.1 Automatic Seed Placement 305
12.5.2 Shape/Size Constraint for Level Set Segmentation 305
12.5.3 Model-Based Fast Marching Cell Phase Tracking 307
12.6 Results 310
12.6.1 Large-Scale Toxicological Study 310
12.6.2 Algorithmic Validation 310
12.7 A Tool for Cell Cycle Research 311
12.7.1 Research Prototype 312
12.7.2 Visualization 313
12.8 Summary and Future Work 314
References 314
Contents x[
Cell Segmentation for Division Rate Estimation in Computerized
Video Time-Lapse Microscopy 317
13.1 Introduction 317
13.2 Methodology 319
13.2.1 Cell Detection with AdaBoost 319
13.2.2 Foreground Segmentation 323
13.2.3 Cytoplasm Segmentation Using the Watershed
Algorithm 326
13.2.4 Cell Division Rate Estimation 327
13.3 Experiments 328
13.4 Conclusions 329
References 329
Automated Cellular and Tissue Analysis
Systems Biology and the Digital Fish Project: A Vast New Frontier
for Image Analysis 331
14.1 Introduction 331
14.2 Imaging-Based Systems Biology 331
14.2.1 What Is Systems Biology? 331
14.2.2 Imaging in Systems Biology 335
14.3 Example: The Digital Fish Project 338
14.3.1 Goals of Project 338
14.3.2 Why Fish? 340
14.3.3 Imaging 340
14.3.4 Image Analysis 347
14.3.5 Visualization 352
14.3.6 Data Analysis 352
14.3.7 Registration/Integration, Reference Atlas 357
14.4 Bridging the Gap 353
14.4.1 Open Source 353
14.4.2 Traversing the Gap 354
14.5 Conclusions 354
References 355
Quantitative Phenotyping Using Microscopic Images 357
15.1 Introduction 357
15.2 Relevant Biomedical Applications 359
xjl Contents
15.2.1 Mouse Model Phenotyping Study: Role of the
Rb Gene 359
15.2.2 Mouse Model Phenotyping Study: The PTEN
Gene and Cancer 359
15.2.3 3-D Reconstruction of Cellular Structure of
Zebrafish Embryo 360
15.3 Tissue Segmentation Using N-Point Correlation Functions 361
15.3.1 Introduction to N-Point Correlation Functions 361
15.3.2 Segmentation of Microscopic Images Using N-pcfs 364
15.4 Segmentation of Individual Cells 364
15.4.1 Modality-Dependent Segmentation: Active
Contour Models 364
15.4.2 Modality-Independent Segmentation: Using
Tessellations 366
15.5 Registration of Large Microscopic Images 371
15.5.1 Rigid Registration 371
15.5.2 Nonrigid Registration 372
15.6 3-D Visualization 375
15.6.1 Mouse Model Phenotyping Study: Role of the
Rb Gene 375
15.6.2 Mouse Model Phenotyping Study: Role of the
PTEN Gene in Cancer 377
15.6.3 Zebrafish Phenotyping Studies 379
15.7 Quantitative Validation 380
15.7.1 Mouse Placenta Phenotyping Studies 380
15.7.2 Mouse Mammary Gland Phenotyping Study 382
15.8 Summary 384
References 389
Automatic 3-D Morphological Reconstruction of Neuron Cells from
Multiphoton Images 389
16.1 Introduction 389
16.2 Materials and Methods 391
16.2.1 Experimental Data 391
16.3 Results 396
16.4 Conclusions 397
References 398
Robust 3-D Reconstruction and Identification of Dendritic Spines 401
17.1 Introduction 401
17.2 Related Work 404
17.3 Image Acquisition and Processing 404
17.3.1 Data-Set 405
17.3.2 Denoising and Resampling 405
Contents xii[
17.3.3 Segmenting the Neuron 407
17.3.4 Floating Spine Heads 408
17.4 Neuron Reconstruction and Analysis 409
17A.I Surfacing and Surface Fairing 410
17.4.2 Curve Skeletonization 412
17.4.3 Dendrite Tree Model 413
17.4.4 Morphometry and Spine Identification 413
17.5 Results 414
17.6 Conclusion 419
References 419
In Vivo Microscopy
Small Critter Imaging 425
18.1 In Vivo Molecular Small Animal Imaging 425
18.1.1 Fluorescence Microscopic Imaging 425
18.1.2 Bioluminescence Imaging 426
18.1.3 Coherent Anti-Stokes Raman Scattering Imaging 426
18.1.4 Fibered In Vivo Imaging 427
18.2 Fluorescence Molecular Imaging (FMT) 427
18.2.1 Fluorescence Scanning 427
18.2.2 FMT Data Processing 428
18.2.3 Multimodality 428
18.3 Registration of 3-D FMT and MicroCT Images 429
18.3.1 Introduction 429
18.3.2 Problem Statement and Formulation 430
18.3.3 Combined Differential Evolution and Simplex
Method Optimization 432
18.3.4 A Novel Optimization Method Based
on Sequential Monte Carlo 436
18.4 Conclusions 438
References 439
Processing of In Vivo Fibered Confocal Microscopy Video Sequences 441
19.1 Motivations 441
19.2 Principles of Fibered Confocal Microscopy 443
19.2.1 Confocal Microscopy 443
19.2.2 Distal Scanning Fibered Confocal Microscopy 443
19.2.3 Proximal Scanning Fibered Confocal Microscopy 444
19.3 Real-Time Fiber Pattern Rejection 447
19.3.1 Calibrated Raw Data Acquisition 447
xiv Contents
19.3.2 Real-Time Processing 448
19.4 Blood Flow Velocimetry Using Motion Artifacts 450
19.4.1 Imaging of Moving Objects 450
19.4.2 Velocimetry Algorithm 451
19.4.3 Results and Evaluation 453
19.5 Region Tracking for Kinetic Analysis 454
19.5.1 Motion Compensation Algorithm 454
19.5.2 Affine Registration Algorithm 455
19.5.3 Application to Cell Trafficking 456
19.6 Mosaicking: Bridging the Gap Between Microscopic and
Macroscopic Scales 457
19.6.1 Overview of the Algorithm 458
19.6.2 Results and Evaluation 460
19.7 Conclusions 461
References 461
About the Editors 465
List of Contributors 467
Index 473 |
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| id | DE-604.BV023393919 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:21:09Z |
| indexdate | 2024-07-09T21:17:37Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016576815 |
| oclc_num | 221163265 |
| open_access_boolean | |
| owner | DE-M49 DE-BY-TUM |
| owner_facet | DE-M49 DE-BY-TUM |
| physical | XXII, 489 S. Ill., graph. Darst. 1 CD-ROM (12 cm) |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Artech House |
| record_format | marc |
| series2 | Bioinformatics & biomedical imaging |
| spelling | Microscopic image analysis for life science applications Jens Rittscher, Stephen T. C. Wong, and Raghu Machiraju, ed. London [u.a.] Artech House 2008 XXII, 489 S. Ill., graph. Darst. 1 CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier Bioinformatics & biomedical imaging Image Enhancement Image Interpretation, Computer-Assisted methods Image analysis Imaging systems Imaging, Three-Dimensional Microscopy Microscopy methods Tissue Array Analysis methods Bildanalyse (DE-588)4145391-8 gnd rswk-swf Mikroskopie (DE-588)4039238-7 gnd rswk-swf Biologie (DE-588)4006851-1 gnd rswk-swf Biologie (DE-588)4006851-1 s Mikroskopie (DE-588)4039238-7 s Bildanalyse (DE-588)4145391-8 s b DE-604 Rittscher, Jens Sonstige oth Wong, Stephen T. C. Sonstige oth Machiraju, Raghu Sonstige oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016576815&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Microscopic image analysis for life science applications Image Enhancement Image Interpretation, Computer-Assisted methods Image analysis Imaging systems Imaging, Three-Dimensional Microscopy Microscopy methods Tissue Array Analysis methods Bildanalyse (DE-588)4145391-8 gnd Mikroskopie (DE-588)4039238-7 gnd Biologie (DE-588)4006851-1 gnd |
| subject_GND | (DE-588)4145391-8 (DE-588)4039238-7 (DE-588)4006851-1 |
| title | Microscopic image analysis for life science applications |
| title_auth | Microscopic image analysis for life science applications |
| title_exact_search | Microscopic image analysis for life science applications |
| title_exact_search_txtP | Microscopic image analysis for life science applications |
| title_full | Microscopic image analysis for life science applications Jens Rittscher, Stephen T. C. Wong, and Raghu Machiraju, ed. |
| title_fullStr | Microscopic image analysis for life science applications Jens Rittscher, Stephen T. C. Wong, and Raghu Machiraju, ed. |
| title_full_unstemmed | Microscopic image analysis for life science applications Jens Rittscher, Stephen T. C. Wong, and Raghu Machiraju, ed. |
| title_short | Microscopic image analysis for life science applications |
| title_sort | microscopic image analysis for life science applications |
| topic | Image Enhancement Image Interpretation, Computer-Assisted methods Image analysis Imaging systems Imaging, Three-Dimensional Microscopy Microscopy methods Tissue Array Analysis methods Bildanalyse (DE-588)4145391-8 gnd Mikroskopie (DE-588)4039238-7 gnd Biologie (DE-588)4006851-1 gnd |
| topic_facet | Image Enhancement Image Interpretation, Computer-Assisted methods Image analysis Imaging systems Imaging, Three-Dimensional Microscopy Microscopy methods Tissue Array Analysis methods Bildanalyse Mikroskopie Biologie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016576815&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT rittscherjens microscopicimageanalysisforlifescienceapplications AT wongstephentc microscopicimageanalysisforlifescienceapplications AT machirajuraghu microscopicimageanalysisforlifescienceapplications |