Microscope Image Processing (eBook)

Front Cover 1
Microscope Image Processing 4
Copyright Page 5
Contents 6
Foreword 22
Reference 23
Preface 24
Acknowledgments 26
Chapter 1: Introduction 30
1.1 The Microscope and Image Processing 30
1.2 Scope of This Book 30
1.3 Our Approach 32
1.3.1 The Four Types of Images 32
1.3.1.1 Optical Image 33
1.3.1.2 Continuous Image 33
1.3.1.3 Digital Image 33
1.3.1.4 Displayed Image 34
1.3.2 The Result 34
1.3.2.1 Analytic Functions 35
1.3.3 The Sampling Theorem 36
1.4 The Challenge 37
1.5 Nomenclature 37
1.6 Summary of Important Points 37
References 38
Chapter 2: Fundamentals of Microscopy 40
2.1 Origins of the Microscope 40
2.2 Optical Imaging 41
2.2.1 Image Formation by a Lens 41
2.2.1.1 Imaging a Point Source 42
2.2.1.2 Focal Length 42
2.2.1.3 Numerical Aperture 43
2.2.1.4 Lens Shape 44
2.3 Diffraction-Limited Optical Systems 44
2.3.1 Linear System Analysis 45
2.4 Incoherent Illumination 45
2.4.1 The Point Spread Function 45
2.4.2 The Optical Transfer Function 46
2.5 Coherent Illumination 47
2.5.1 The Coherent Point Spread Function 47
2.5.2 The Coherent Optical Transfer Function 48
2.6 Resolution 49
2.6.1 Abbe Distance 50
2.6.2 Rayleigh Distance 50
2.6.3 Size Calculations 50
2.7 Aberration 51
2.8 Calibration 51
2.8.1 Spatial Calibration 52
2.8.2 Photometric Calibration 52
2.9 Summary of Important Points 53
References 54
Chapter 3: Image Digitization 56
3.1 Introduction 56
3.2 Resolution 57
3.3 Sampling 58
3.3.1 Interpolation 59
3.3.2 Aliasing 61
3.4 Noise 62
3.5 Shading 63
3.6 Photometry 63
3.7 Geometric Distortion 64
3.8 Complete System Design 64
3.8.1 Cumulative Resolution 64
3.8.2 Design Rules of Thumb 65
3.8.2.1 Pixel Spacing 65
3.8.2.2 Resolution 65
3.8.2.3 Noise 65
3.8.2.4 Photometry 65
3.8.2.5 Distortion 66
3.9 Summary of Important Points 66
References 66
Chapter 4: Image Display 68
4.1 Introduction 68
4.2 Display Characteristics 69
4.2.1 Displayed Image Size 69
4.2.2 Aspect Ratio 69
4.2.3 Photometric Resolution 70
4.2.4 Grayscale Linearity 71
4.2.5 Low-Frequency Response 71
4.2.5.1 Pixel Polarity 71
4.2.5.2 Pixel Interaction 72
4.2.6 High-Frequency Response 72
4.2.7 The Spot-Spacing Compromise 72
4.2.8 Noise Considerations 72
4.3 Volatile Displays 73
4.4 Sampling for Display Purposes 74
4.4.1 Oversampling 75
4.4.2 Resampling 75
4.5 Display Calibration 76
4.6 Summary of Important Points 76
References 77
Chapter 5: Geometric Transformations 80
5.1 Introduction 80
5.2 Implementation 81
5.3 Gray-Level Interpolation 81
5.3.1 Nearest-Neighbor Interpolation 82
5.3.2 Bilinear Interpolation 82
5.3.3 Bicubic Interpolation 83
5.3.4 Higher-Order Interpolation 83
5.4 Spatial Transformation 84
5.4.1 Control-Grid Mapping 84
5.5 Applications 85
5.5.1 Distortion Removal 85
5.5.2 Image Registration 85
5.5.3 Stitching 85
5.6 Summary of Important Points 86
References 86
Chapter 6: Image Enhancement 88
6.1 Introduction 88
6.2 Spatial Domain Methods 89
6.2.1 Contrast Stretching 89
6.2.2 Clipping and Thresholding 90
6.2.3 Image Subtraction and Averaging 90
6.2.4 Histogram Equalization 91
6.2.5 Histogram Specification 91
6.2.6 Spatial Filtering 92
6.2.7 Directional and Steerable Filtering 94
6.2.8 Median Filtering 96
6.3 Fourier Transform Methods 97
6.3.1 Wiener Filtering and Wiener Deconvolution 97
6.3.2 Deconvolution Using a Least-Squares Approach 99
6.3.3 Low-Pass Filtering in the Fourier Domain 100
6.3.4 High-Pass Filtering in the Fourier Domain 100
6.4 Wavelet Transform Methods 101
6.4.1 Wavelet Thresholding 101
6.4.2 Differential Wavelet Transform and Multiscale Pointwise Product 102
6.5 Color Image Enhancement 103
6.5.1 Pseudo-Color Transformations 104
6.5.2 Color Image Smoothing 104
6.5.3 Color Image Sharpening 104
6.6 Summary of Important Points 105
References 106
Chapter 7: Wavelet Image Processing 108
7.1 Introduction 108
7.1.1 Linear Transformations 109
7.1.2 Short-Time Fourier Transform and Wavelet Transform 110
7.2 Wavelet Transforms 112
7.2.1 Continuous Wavelet Transform 112
7.2.2 Wavelet Series Expansion 113
7.2.3 Haar Wavelet Functions 114
7.3 Multiresolution Analysis 114
7.3.1 Multiresolution and Scaling Function 115
7.3.2 Scaling Functions and Wavelets 116
7.4 Discrete Wavelet Transform 117
7.4.1 Decomposition 117
7.4.2 Reconstruction 120
7.4.3 Filter Banks 121
7.4.3.1 Two-Channel Subband Coding 121
7.4.3.2 Orthogonal Filter Design 122
7.4.4 Compact Support 124
7.4.5 Biorthogonal Wavelet Transforms 125
7.4.5.1 Biorthogonal Filter Banks 126
7.4.5.2 Examples of Biorthogonal Wavelets 128
7.4.6 Lifting Schemes 129
7.4.6.1 Biorthogonal Wavelet Design 129
7.4.6.2 Wavelet Transform Using Lifting 130
7.5 Two-Dimensional Discrete Wavelet Transform 131
7.5.1 Two-Dimensional Wavelet Bases 131
7.5.2 Forward Transform 132
7.5.3 Inverse Transform 134
7.5.4 Two-Dimensional Biorthogonal Wavelets 134
7.5.5 Overcomplete Transforms 135
7.6 Examples 136
7.6.1 Image Compression 136
7.6.2 Image Enhancement 136
7.6.3 Extended Depth-of-Field by Wavelet Image Fusion 137
7.7 Summary of Important Points 137
References 139
Chapter 8: Morphological Image Processing 142
8.1 Introduction 142
8.2 Binary Morphology 144
8.2.1 Binary Erosion and Dilation 144
8.2.2 Binary Opening and Closing 145
8.2.3 Binary Morphological Reconstruction from Markers 147
8.2.3.1 Connectivity 147
8.2.3.2 Markers 148
8.2.3.3 The Edge-Off Operation 149
8.2.4 Reconstruction from Opening 149
8.2.5 Area Opening and Closing 151
8.2.6 Skeletonization 152
8.3 Grayscale Operations 156
8.3.1 Threshold Decomposition 157
8.3.2 Erosion and Dilation 158
8.3.2.1 Gradient 160
8.3.3 Opening and Closing 160
8.3.3.1 Top-Hat Filtering 160
8.3.3.2 Alternating Sequential Filters 162
8.3.4 Component Filters and Grayscale Morphological Reconstruction 163
8.3.4.1 Morphological Reconstruction 164
8.3.4.2 Alternating Sequential Component Filters 164
8.3.4.3 Grayscale Area Opening and Closing 164
8.3.4.4 Edge-Off Operator 165
8.3.4.5 h-Maxima and h-Minima Operations 166
8.3.4.6 Regional Maxima and Minima 166
8.3.4.7 Regional Extrema as Markers 167
8.4 Watershed Segmentation 167
8.4.1 Classical Watershed Transform 168
8.4.2 Filtering the Minima 169
8.4.3 Texture Detection 172
8.4.4 Watershed from Markers 174
8.4.5 Segmentation of Overlapped Convex Cells 175
8.4.6 Inner and Outer Markers 177
8.4.7 Hierarchical Watershed 180
8.4.8 Watershed Transform Algorithms 181
8.5 Summary of Important Points 183
References 185
Chapter 9: Image Segmentation 188
9.1 Introduction 188
9.1.1 Pixel Connectivity 189
9.2 Region-Based Segmentation 189
9.2.1 Thresholding 189
9.2.1.1 Global Thresholding 190
9.2.1.2 Adaptive Thresholding 191
9.2.1.3 Threshold Selection 192
9.2.1.4 Thresholding Circular Spots 194
9.2.1.5 Thresholding Noncircular and Noisy Spots 196
9.2.2 Morphological Processing 198
9.2.2.1 Hole Filling 200
9.2.2.2 Border-Object Removal 200
9.2.2.3 Separation of Touching Objects 201
9.2.2.4 The Watershed Algorithm 201
9.2.3 Region Growing 202
9.2.4 Region Splitting 204
9.3 Boundary-Based Segmentation 205
9.3.1 Boundaries and Edges 205
9.3.2 Boundary Tracking Based on Maximum Gradient Magnitude 206
9.3.3 Boundary Finding Based on Gradient Image Thresholding 207
9.3.4 Boundary Finding Based on Laplacian Image Thresholding 208
9.3.5 Boundary Finding Based on Edge Detection and Linking 209
9.3.5.1 Edge Detection 209
9.3.5.2 Edge Linking and Boundary Refinement 212
9.3.6 Encoding Segmented Images 217
9.3.6.1 Object Label Map 218
9.3.6.2 Boundary Chain Code 218
9.4 Summary of Important Points 219
References 221
Chapter 10: Object Measurement 224
10.1 Introduction 224
10.2 Measures for Binary Objects 225
10.2.1 Size Measures 225
10.2.1.1 Area 225
10.2.1.2 Perimeter 225
10.2.1.3 Area and Perimeter of a Polygon 226
10.2.2 Pose Measures 228
10.2.2.1 Centroid 228
10.2.2.2 Orientation 229
10.2.3 Shape Measures 229
10.2.3.1 Thinness Ratio 230
10.2.3.2 Rectangularity 230
10.2.3.3 Circularity 230
10.2.3.4 Euler Number 232
10.2.3.5 Moments 232
10.2.3.6 Elongation 234
10.2.4 Shape Descriptors 235
10.2.4.1 Differential Chain Code 235
10.2.4.2 Fourier Descriptors 235
10.2.4.3 Medial Axis Transform 236
10.2.4.4 Graph Representations 237
10.3 Distance Measures 238
10.3.1 Euclidean Distance 238
10.3.2 City-Block Distance 238
10.3.3 Chessboard Distance 239
10.4 Gray-Level Object Measures 239
10.4.1 Intensity Measures 239
10.4.1.1 Integrated Optical Intensity 239
10.4.1.2 Average Optical Intensity 239
10.4.1.3 Contrast 240
10.4.2 Histogram Measures 240
10.4.2.1 Mean Gray Level 240
10.4.2.2 Standard Deviation of Gray Levels 240
10.4.2.3 Skew 241
10.4.2.4 Entropy 241
10.4.2.5 Energy 241
10.4.3 Texture Measures 241
10.4.3.1 Statistical Texture Measures 242
10.4.3.2 Power Spectrum Features 243
10.5 Object Measurement Considerations 244
10.6 Summary of Important Points 244
References 246
Chapter 11: Object Classification 250
11.1 Introduction 250
11.2 The Classification Process 250
11.2.1 Bayes’ Rule 251
11.3 The Single-Feature, Two-Class Case 251
11.3.1 A Priori Probabilities 252
11.3.2 Conditional Probabilities 252
11.3.3 Bayes’ Theorem 253
11.4 The Three-Feature, Three-Class Case 254
11.4.1 Bayes Classifier 255
11.4.1.1 Prior Probabilities 255
11.4.1.2 Classifier Training 256
11.4.1.3 The Mean Vector 256
11.4.1.4 Covariance 257
11.4.1.5 Variance and Standard Deviation 257
11.4.1.6 Correlation 257
11.4.1.7 The Probability Density Function 258
11.4.1.8 Classification 258
11.4.1.9 Log Likelihoods 258
11.4.1.10 Mahalanobis Distance Classifier 259
11.4.1.11 Uncorrelated Features 259
11.4.2 A Numerical Example 260
11.5 Classifier Performance 261
11.5.1 The Confusion Matrix 262
11.6 Bayes Risk 263
11.6.1 Minimum-Risk Classifier 263
11.7 Relationships Among Bayes Classifiers 264
11.8 The Choice of a Classifier 264
11.8.1 Subclassing 265
11.8.2 Feature Normalization 265
11.9 Nonparametric Classifiers 267
11.9.1 Nearest-Neighbor Classifiers 268
11.10 Feature Selection 269
11.10.1 Feature Reduction 269
11.10.1.1 Principal Component Analysis 270
11.10.1.2 Linear Discriminant Analysis 271
11.11 Neural Networks 272
11.12 Summary of Important Points 273
References 274
Chapter 12: Fluorescence Imaging 276
12.1 Introduction 276
12.2 Basics of Fluorescence Imaging 277
12.2.1 Image Formation in Fluorescence Imaging 278
12.3 Optics in Fluorescence Imaging 279
12.4 Limitations in Fluorescence Imaging 280
12.4.1 Instrumentation-Based Aberrations 280
12.4.1.1 Photon Shot Noise 280
12.4.1.2 Dark Current 281
12.4.1.3 Auxiliary Noise Sources 281
12.4.1.4 Quantization Noise 282
12.4.1.5 Other Noise Sources 282
12.4.2 Sample-Based Aberrations 282
12.4.2.1 Photobleaching 282
12.4.2.2 Autofluorescence 283
12.4.2.3 Absorption and Scattering 284
12.4.3 Sample and Instrumentation Handling–Based Aberrations 284
12.5 Image Corrections in Fluorescence Microscopy 285
12.5.1 Background Shading Correction 285
12.5.2 Correction Using the Recorded Image 286
12.5.3 Correction Using Calibration Images 287
12.5.3.1 Two-Image Calibration 287
12.5.3.2 Background Subtraction 287
12.5.4 Correction Using Surface Fitting 288
12.5.5 Histogram-Based Background Correction 290
12.5.6 Other Approaches for Background Correction 290
12.5.7 Autofluorescence Correction 290
12.5.8 Spectral Overlap Correction 291
12.5.9 Photobleaching Correction 291
12.5.10 Correction of Fluorescence Attenuation in Depth 294
12.6 Quantifying Fluorescence 295
12.6.1 Fluorescence Intensity Versus Fluorophore Concentration 295
12.7 Fluorescence Imaging Techniques 296
12.7.1 Immunofluorescence 296
12.7.2 Fluorescence in situ Hybridization (FISH) 299
12.7.3 Quantitative Colocalization Analysis 300
12.7.4 Fluorescence Ratio Imaging (RI) 304
12.7.5 Fluorescence Resonance Energy Transfer (FRET) 306
12.7.6 Fluorescence Lifetime Imaging (FLIM) 313
12.7.7 Fluorescence Recovery After Photobleaching (FRAP) 315
12.7.8 Total Internal Reflectance Fluorescence Microscopy (TIRFM) 317
12.7.9 Fluorescence Correlation Spectroscopy (FCS) 318
12.8 Summary of Important Points 319
References 320
Chapter 13: Multispectral Imaging 328
13.1 Introduction 328
13.2 Principles of Multispectral Imaging 329
13.2.1 Spectroscopy 330
13.2.2 Imaging 331
13.2.3 Multispectral Microscopy 333
13.2.4 Spectral Image Acquisition Methods 333
13.2.4.1 Wavelength-Scan Methods 333
13.2.4.2 Spatial-Scan Methods 334
13.2.4.3 Time-Scan Methods 335
13.3 Multispectral Image Processing 335
13.3.1 Calibration for Multispectral Image Acquisition 336
13.3.2 Spectral Unmixing 341
13.3.2.1 Fluorescence Unmixing 344
13.3.2.2 Brightfield Unmixing 346
13.3.2.3 Unsupervised Unmixing 347
13.3.3 Spectral Image Segmentation 350
13.3.3.1 Combining Segmentation with Classification 351
13.3.3.2 M-FISH Pixel Classification 351
13.4 Summary of Important Points 352
References 353
Chapter 14: Three-Dimensional Imaging 358
14.1 Introduction 358
14.2 Image Acquisition 358
14.2.1 Wide-Field Three-Dimensional Microscopy 359
14.2.2 Confocal Microscopy 359
14.2.3 Multiphoton Microscopy 360
14.2.4 Other Three-Dimensional Microscopy Techniques 362
14.3 Three-Dimensional Image Data 363
14.3.1 Three-Dimensional Image Representation 363
14.3.1.1 Three-Dimensional Image Notation 363
14.4 Image Restoration and Deblurring 364
14.4.1 The Point Spread Function 364
14.4.1.1 Theoretical Model of the Point Spread Function 366
14.4.2 Models for Microscope Image Formation 367
14.4.2.1 Poisson Noise 367
14.4.2.2 Gaussian Noise 367
14.4.3 Algorithms for Deblurring and Restoration 368
14.4.3.1 No-Neighbor Methods 368
14.4.3.2 Nearest-Neighbor Method 369
14.4.3.3 Linear Methods 371
14.4.3.4 Nonlinear Methods 375
14.4.3.5 Maximum-Likelihood Restoration 378
14.4.3.6 Blind Deconvolution 382
14.4.3.7 Interpretation of Deconvolved Images 383
14.4.3.8 Commercial Deconvolution Packages 383
14.5 Image Fusion 384
14.6 Three-Dimensional Image Processing 385
14.7 Geometric Transformations 385
14.8 Pointwise Operations 386
14.9 Histogram Operations 386
14.10 Filtering 388
14.10.1 Linear Filters 388
14.10.1.1 Finite Impulse Response (FIR) Filter 388
14.10.2 Nonlinear Filters 389
14.10.2.1 Median Filter 389
14.10.2.2 Weighted Median Filter 389
14.10.2.3 Minimum and Maximum Filters 390
14.10.2.4 alpha-Trimmed Mean Filters 390
14.10.3 Edge-Detection Filters 390
14.11 Morphological Operators 391
14.11.1 Binary Morphology 392
14.11.2 Grayscale Morphology 393
14.12 Segmentation 394
14.12.1 Point-Based Segmentation 395
14.12.2 Edge-Based Segmentation 396
14.12.3 Region-Based Segmentation 398
14.12.3.1 Connectivity 398
14.12.3.2 Region Growing 399
14.12.3.3 Region Splitting and Region Merging 399
14.12.4 Deformable Models 400
14.12.5 Three-Dimensional Segmentation Methods in the Literature 401
14.13 Comparing Three-Dimensional Images 404
14.14 Registration 404
14.15 Object Measurements in Three Dimensions 405
14.15.1 Euler Number 405
14.15.2 Bounding Box 406
14.15.3 Center of Mass 406
14.15.4 Surface Area Estimation 407
14.15.5 Length Estimation 408
14.15.6 Curvature Estimation 409
14.15.6.1 Surface Triangulation Method 410
14.15.6.2 Cross-Patch Method 410
14.15.7 Volume Estimation 410
14.15.8 Texture 411
14.16 Three-Dimensional Image Display 411
14.16.1 Montage 411
14.16.2 Projected Images 413
14.16.2.1 Voxel Projection 413
14.16.2.2 Ray Casting 413
14.16.3 Surface and Volume Rendering 414
14.16.3.1 Surface Rendering 414
14.16.3.2 Volume Rendering 415
14.16.4 Stereo Pairs 416
14.16.5 Color Anaglyphs 417
14.16.6 Animations 417
14.17 Summary of Important Points 418
References 421
Chapter 15: Time-Lapse Imaging 430
15.1 Introduction 430
15.2 Image Acquisition 432
15.2.1 Microscope Setup 433
15.2.2 Spatial Dimensionality 434
15.2.3 Temporal Resolution 439
15.3 Image Preprocessing 440
15.3.1 Image Denoising 440
15.3.2 Image Deconvolution 441
15.3.3 Image Registration 442
15.4 Image Analysis 443
15.4.1 Cell Tracking 444
15.4.1.1 Cell Segmentation 444
15.4.1.2 Cell Association 446
15.4.2 Particle Tracking 446
15.4.2.1 Particle Detection 447
15.4.2.2 Particle Association 448
15.5 Trajectory Analysis 449
15.5.1 Geometry Measurements 450
15.5.2 Diffusivity Measurements 450
15.5.3 Velocity Measurements 452
15.6 Sample Algorithms 452
15.6.1 Cell Tracking 453
15.6.2 Particle Tracking 456
15.7 Summary of Important Points 461
References 463
Chapter 16: Autofocusing 470
16.1 Introduction 470
16.1.1 Autofocus Methods 470
16.1.2 Passive Autofocusing 471
16.2 Principles of Microscope Autofocusing 471
16.2.1 Fluorescence and Brightfield Autofocusing 472
16.2.2 Autofocus Functions 473
16.2.3 Autofocus Function Sampling and Approximation 474
16.2.3.1 Gaussian Fitting 476
16.2.3.2 Parabola Fitting 476
16.2.4 Finding the In-Focus Imaging Position 477
16.3 Multiresolution Autofocusing 477
16.3.1 Multiresolution Image Representations 478
16.3.2 Wavelet-Based Multiresolution Autofocus Functions 480
16.3.3 Multiresolution Search for In-Focus Position 480
16.4 Autofocusing for Scanning Microscopy 481
16.5 Extended Depth-of-Field Microscope Imaging 483
16.5.1 Digital Image Fusion 484
16.5.2 Pixel-Based Image Fusion 485
16.5.3 Neighborhood-Based Image Fusion 486
16.5.4 Multiresolution Image Fusion 487
16.5.5 Noise and Artifact Control in Image Fusion 488
16.5.5.1 Multiscale Pointwise Product 489
16.5.5.2 Consistency Checking 490
16.5.5.3 Reassignment 491
16.6 Examples 491
16.7 Summary of Important Points 491
References 494
Chapter 17: Structured Illumination Imaging 498
17.1 Introduction 498
17.1.1 Conventional Light Microscope 498
17.1.2 Sectioning the Specimen 499
17.1.3 Structured Illumination 500
17.2 Linear SIM Instrumentation 501
17.2.1 Spatial Light Modulator 502
17.3 The Process of Structured Illumination Imaging 502
17.3.1 Extended-Depth-of-Field Image 504
17.3.2 SIM for Optical Sectioning 504
17.3.3 Sectioning Strength 506
17.4 Limitations of Optical Sectioning with SIM 508
17.4.1 Artifact Reduction via Image Processing 509
17.4.1.1 Intensity Normalization 509
17.4.1.2 Grid Position Error 511
17.4.1.3 Statistical Waveform Compensation 513
17.4.1.4 Parameter Optimization 514
17.5 Color Structured Illumination 515
17.5.1 Processing Technique 516
17.5.2 Chromatic Aberration 517
17.5.3 SIM Example 519
17.6 Lateral Superresolution 520
17.6.1 Bypassing the Optical Transfer Function 520
17.6.2 Mathematical Foundation 521
17.6.2.1 Shifting Frequency Space 521
17.6.2.2 Extracting the Enhanced Image 522
17.6.3 Lateral Resolution Enhancement Simulation 524
17.7 Summary of Important Points 525
References 525
Chapter 18: Image Data and Workflow Management 528
18.1 Introduction 528
18.1.1 Open Microscopy Environment 529
18.1.2 Image Management in Other Fields 529
18.1.3 Requirements for Microscopy Image Management Systems 529
18.2 Architecture of Microscopy Image/Data/Workflow Systems 530
18.2.1 Client–Server Architecture 530
18.2.2 Image and Data Servers 531
18.2.3 Users, Ownership, Permissions 532
18.3 Microscopy Image Management 533
18.3.1 XYZCT Five-Dimensional Imaging Model 533
18.3.2 Image Viewers 533
18.3.3 Image Hierarchies 535
18.3.3.1 Predefined Containers 535
18.3.3.2 User-Defined Containers 536
18.3.4 Browsing and Search 537
18.3.5 Microscopy Image File Formats and OME-XML 538
18.3.5.1 OME-XML Image Acquisition Ontology 540
18.4 Data Management 541
18.4.1 Biomedical Ontologies 542
18.4.2 Building Ontologies with OME SemanticTypes 543
18.4.3 Data Management Software with Plug-in Ontologies 545
18.4.4 Storing Data with Ontological Structure 546
18.4.4.1 Image Acquisition Meta-Data 546
18.4.4.2 Mass Annotations 546
18.4.4.3 Spreadsheet Annotations 547
18.5 Workflow Management 548
18.5.1 Data Provenance 548
18.5.1.1 OME AnalysisModules 549
18.5.1.2 Editing and Deleting Data 549
18.5.2 Modeling Quantitative Image Analysis 550
18.5.2.1 Coupling Algorithms to Informatics Platforms 551
18.5.2.2 Composing Workflows 553
18.5.2.3 Enacting Workflows 553
18.6 Summary of Important Points 556
References 558
Glossary of Microscope Image Processing Terms 560
References 568
Index 570
Color Plate Section 578