Table Of Contents:
Foreword xv
Preface xvii

PART I Introduction

Introduction to Biological Light Microscopy 1(18)

Introduction 1(1)

Need for Microscopy 1(1)

Image Formation in Transmitted Light Microscopy 2(3)

Resolution, Magnification, and Contrast in Microscopy 5(2)

Phase Contrast Microscopy 7(1)

Dark Field Microscopy 8(1)

Polarization Microscopy 8(1)

Differential Interference Contrast Microscopy 9(1)

Reflected Light Microscopy 10(2)

Fluorescence Microscopy 12(1)

Light Microscopy in Biology 12(2)

Noise and Artifacts in Microscopic Images 14(2)

Trends in Light Microscopy 16(3)

References 17(2)

Molecular Probes for Fluorescence Microscopy 19(30)

Introduction 19(2)

Basic Characteristics of Fluorophores 21(4)

Traditional Fluorescent Dyes 25(1)

Alexa Fluor Dyes 26(2)

Cyanine Dyes 28(1)

Fluorescent Environmental Probes 29(3)

Organelle Probes 32(2)

Quantum Dots 34(2)

Fluorescent Proteins 36(3)

Hybrid Systems 39(1)

Quenching and Photobleaching 40(3)

Conclusions 43(6)

References 43(5)

Selected Bibliography 48(1)

Overview of Image Analysis Tools and Tasks for Microscopy 49(36)

Image Analysis Framework 50(6)

Continuous-Domain Image Processing 51(3)

A/D Conversion 54(1)

Discrete-Domain Image Processing 55(1)

Image Analysis Tools for Microscopy 56(12)

Signal and Image Representations 57(3)

Fourier Analysis 60(1)

Gabor Analysis 61(1)

Multiresolution Analysis 62(2)

Unsupervised, Data-Driven Representation and Analysis Methods 64(3)

Statistical Estimation 67(1)

Imaging Tasks in Microscopy 68(11)

Intelligent Acquisition 68(1)

Deconvolution, Denoising, and Restoration 69(3)

Registration and Mosaicking 72(2)

Segmentation, Tracing, and Tracking 74(3)

Classification and Clustering 77(1)

Modeling 78(1)

Conclusions 79(6)

References 79(6)

An Introduction to Fluorescence Microscopy: Basic Principles, Challenges, and Opportunities 85(30)

Fluorescence in Molecular and Cellular Biology 86(5)

The Physical Principles of Fluorescence 86(2)

The Green Revolution 88(3)

Microscopes and Image Formation 91(4)

The Widefield Microscope 91(2)

The Confocal Scanning Microscope 93(1)

Sample Setup and Aberrations 94(1)

Detectors 95(3)

Characteristic Parameters of Detection Systems 95(1)

Detection Technologies 96(2)

Limiting Factors of Fluorescence Imaging 98(1)

Noise Sources 98(1)

Sample-Dependent Limitations 99(1)

Advanced Experimental Techniques 99(4)

FRET 100(1)

FRAP 101(1)

FLIM 102(1)

Signal and Image Processing Challenges 103(4)

Data Size and Dimensionality 103(1)

Image Preparation 103(1)

Restoration 104(1)

Registration 105(1)

Segmentation 106(1)

Quantitative Analysis 106(1)

Current and Future Trends 107(3)

Fluorescent Labels 107(1)

Advanced Microscopy Systems 108(1)

Super-Resolution: Photoactivated Localization-Based Techniques 109(1)

Conclusions 110(5)

References 111(4)

Farsight: A Divide and Conquer Methodology for Analyzing Complex and Dynamic Biological Microenvironments 115(38)

Introduction 115(7)

A Divide-and-Conquer Segmentation Strategy 122(9)

Computing and Representing Image-Based Measurements 131(4)

Analysis of Spatio-Temporal Associations 135(7)

Validation of Automated Image Analysis Results 142(3)

Summary, Discussion, and Future Directions 145(8)

References 146(7)

PART II 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(16)

Introduction 153(1)

MST 154(1)

MST-Cut for Clustering of Coexpressed/Coregulated Genes 154(6)

MST-Cut Clustering Algorithm 155(2)

Embryonic Image Clustering 157(3)

Experiments 160(6)

Performance of MST-Cut on Synthetic Datasets 160(3)

Detection of Coregulated Genes and Regulatory Motifs 163(3)

Conclusions 166(3)

References 167(1)

Selected Bibliography 168(1)

Simulation and Estimation of Intracellular Dynamics and Trafficking 169(22)

Context 169(3)

Introduction to Intracellular Traffic 170(1)

Introduction to Living Cell Microscopy 171(1)

Modeling and Simulation Framework 172(6)

Intracellular Trafficking Models in Video-Microscopy 172(2)

Intracellular Traffic Simulation 174(3)

Example 177(1)

Background Estimation in Video-Microscopy 178(3)

Pixel-Wise Estimation 178(1)

Spatial Coherence for Background Estimation 179(2)

Example 181(1)

Foreground Analysis: Network Tomography 181(6)

Network Tomography Principle 182(2)

Measurements 184(1)

Problem Optimization 185(2)

Experiments 187(1)

Conclusions 187(4)

References 188(3)

Techniques for Cellular and Tissue-Based Image Quantitation of Protein Biomarkers 191(18)

Current Methods for Histological and Tissue-Based Biomarker 191(1)

Multiplexing 192(2)

Fluorescence Microscopy 192(1)

Fluorescent Dyes 193(1)

Quantum Dots 193(1)

Photobleaching 194(1)

Image Analysis 194(8)

Image Preprocessing 195(2)

Image Registration 197(2)

Image Segmentation 199(1)

A Unified Segmentation Algorithm 200(2)

Segmentation of Cytoplasm and Epithelial Regions 202(1)

Multichannel Segmentation Techniques 202(1)

Quantitation of Subcellular Biomarkers 203(1)

Summary 204(5)

References 204(5)

Methods for High-Content, High-Throughput, Image-Based Cell Screeing 209(14)

Introduction 209(1)

Challenges in Image-Based High-Content Screening 210(1)

Methods 210(8)

Illumination and Staining Correction 210(2)

Segmentation 212(2)

Measurements 214(1)

Spatial Bias Correction 214(1)

Exploration and Inference 215(3)

Discussion 218(5)

References 219(4)

PART III Structure and Dynamics of Cell Populations

Particle Tracking in 3D+t Biological Imaging 223(60)

Introduction 223(2)

Main Tracking Methods 225(4)

Autocorrelation Methods 225(1)

Deterministic Methods 226(1)

Multiple Particle Tracking Methods 226(1)

Bayesian Methods 227(2)

Analysis of Bayesian Filters 229(21)

The Conceptual Filter 229(2)

The Kalman Filter 231(3)

The Filter Based on A Grid 234(1)

The Extended Kalman Filter 235(3)

The Interacting Multiple Model Filter 238(4)

The Approximated Filter Based on a Grid 242(3)

The Particle Filter 245(5)

Description of the Main Association Methods 250(8)

The Nearest Neighbor (ML) 252(2)

Multihypothesis Tracking (MHT) 254(1)

The Probabilistic Data Association Filter (PDAF) 255(2)

Joint PDAF (JPDAF) 257(1)

Particle Tracking: Methods for Biological Imaging 258(7)

Proposed Dynamic Models for the IMM 259(2)

Adaptive Validation Gate 261(3)

Association 264(1)

Applications 265(4)

Validation on Synthetic Data 265(2)

Applications to Cell Biology 267(2)

Conclusions 269(7)

References 270(6)

Appendix 10A Pseudocodes for the Algorithms 276(7)

Automated Analysis of the Mitotic Phases of Human Cells in 3-D Fluorescence Microscopy Image Sequences 283(12)

Introduction 283(1)

Methods 284(6)

Image Analysis Workflow 284(1)

Segmentation of Multicell Images 285(2)

Tracking of Mitotic Cell Nuclei 287(1)

Extraction of Static and Dynamic Features 288(1)

Classification 289(1)

Experimental Results 290(2)

Image Data 290(1)

Classification Results 290(2)

Discussion and Conclusion 292(3)

References 292(3)

Automated Spatio-Temporal Cell Cycle Phase Analysis Based on Covert GFP Sensors 295(22)

Introduction 295(1)

Biological Background 296(4)

Cell Cycle Phases 296(1)

Cell Cycle Checkpoints 297(1)

Cell Staining 298(1)

Problem Statement 299(1)

State of the Art 300(2)

Mathematical Framework: Level Sets 302(2)

Active Contours with Edges 303(1)

Active Contours Without Edges 303(1)

Spatio-Temporal Cell Cycle Phase Analysis 304(6)

Automatic Seed Placement 305(1)

Shape/Size Constraint for Level Set Segmentation 305(2)

Model-Based Fast Marching Cell Phase Tracking 307(3)

Results 310(1)

Large-Scale Toxicological Study 310(1)

Algorithmic Validation 310(1)

A Tool for Cell Cycle Research 311(3)

Research Prototype 312(1)

Visualization 313(1)

Summary and Future Work 314(3)

References 314(3)

Cell segmentation for Division Rate Estimation in Computerized Video Time-Lapse Microscopy 317(14)

Introduction 317(2)

Methodology 319(9)

Cell Detection with AdaBoost 319(4)

Foreground Segmentation 323(3)

Cytoplasm Segmentation Using the Watershed Algorithm 326(1)

Cell Division Rate Estimation 327(1)

Experiments 328(1)

Conclusions 329(2)

References 329(2)

PART IV Automated Cellular and Tissue Analysis

Systems Biology and the Digital Fish Project: A Vast New Frontier for Image Analysis 331(26)

Introduction 331(1)

Imaging-Based Systems Biology 331(7)

What Is Systems Biology? 331(4)

Imaging in Systems Biology 335(3)

Example: The Digital Fish Project 338(15)

Goals of Project 338(2)

Why Fish? 340(1)

Imaging 340(7)

Image Analysis 347(5)

Visualization 352(1)

Data Analysis 352(5)

Registration/Integration, Reference Atlas 357

Bridging the Gap 353(1)

Open Source 353(1)

Traversing the Gap 354(1)

Conclusions 354(3)

References 355(2)

Quantitative Phenotyping Using Microscopic Images 357(32)

Introduction 357(2)

Relevant Biomedical Applications 359(2)

Mouse Model Phenotyping Study: Role of the Rb Gene 359(1)

Mouse Model Phenotyping Study: The PTEN Gene and Cancer 359(1)

3-D Reconstruction of Cellular Structure of Zebrafish Embryo 360(1)

Tissue Segmentation Using N-Point Correlation Functions 361(3)

Introduction to N-Point Correlation Functions 361(3)

Segmentation of Microscopic Images Using N-pcfs 364(1)

Segmentation of Individual Cells 364(7)

Modality-Dependent Segmentation: Active Contour Models 364(2)

Modality-Independent Segmentation: Using Tessellations 366(5)

Registration of Large Microscopic Images 371(4)

Rigid Registration 371(1)

Nonrigid Registration 372(3)

3-D Visualization 375(5)

Mouse Model Phenotyping Study: Role of the Rb Gene 375(2)

Mouse Model Phenotyping Study: Role of the PTEN Gene in Cancer 377(2)

Zebrafish Phenotyping Studies 379(1)

Quantitative Validation 380(4)

Mouse Placenta Phenotyping Studies 380(2)

Mouse Mammary Gland Phenotyping Study 382(2)

Summary 384(5)

References 389(1)

Automatic 3-D Morphological Reconstruction of Neuron Cells from Multiphoton Images 389(12)

Introduction 389(2)

Materials and Methods 391(5)

Experimental Data 391(5)

Results 396(1)

Conclusions 397(4)

References 398(3)

Robust 3-D Reconstruction and Identification of Dendritic Spines 401(24)

Introduction 401(3)

Related Work 404(1)

Image Acquisition and Processing 404(5)

Data-Set 405(1)

Denoising and Resampling 405(2)

Segmenting the Neuron 407(1)

Floating Spine Heads 408(1)

Neuron Reconstruction and Analysis 409(5)

Surfacing and Surface Fairing 410(2)

Curve Skeletonization 412(1)

Dendrite Tree Model 413(1)

Morphometry and Spine Identification 413(1)

Results 414(5)

Conclusion 419(6)

References 419(6)

PART V In Vivo Microscopy

Small Critter Imaging 425(16)

In Vivo Molecular Small Animal Imaging 425(2)

Fluorescence Microscopic Imaging 425(1)

Bioluminescence Imaging 426(1)

Coherent Anti-Stokes Raman Scattering Imaging 426(1)

Fibered In Vivo Imaging 427(1)

Fluorescence Molecular Imaging (FMT) 427(2)

Fluorescence Scanning 427(1)

FMT Data Processing 428(1)

Multimodality 428(1)

Registration of 3-D FMT and MicroCT Images 429(9)

Introduction 429(1)

Problem Statement and Formulation 430(2)

Combined Differential Evolution and Simplex Method Optimization 432(4)

A Novel Optimization Method Based on Sequential Monte Carlo 436(2)

Conclusions 438(3)

References 439(2)

Processing of In Vivo Fibered Confocal Microscopy Video Sequences 441(24)

Motivations 441(2)

Principles of Fibered Confocal Microscopy 443(4)

Confocal Microscopy 443(1)

Distal Scanning Fibered Confocal Microscopy 443(1)

Proximal Scanning Fibered Confocal Microscopy 444(3)

Real-Time Fiber Pattern Rejection 447(3)

Calibrated Raw Data Acquistion 447(1)

Real-Time Processing 448(2)

Blood Flow Velocimetry Using Motion Artifacts 450(4)

Imaging of Moving Objects 450(1)

Velocimetry Algorithm 451(2)

Results and Evaluation 453(1)

Region Tracking for Kinetic Analysis 454(3)

Motion Compensation Algorithm 454(1)

Affine Registration Algorithm 455(1)

Application to Cell Trafficking 456(1)

Mosaicking: Bridging the Gap Between Microscopic and Macroscopic Scales 457(4)

Overview of the Algorithm 458(2)

Results and Evaluation 460(1)

Conclusions 461(4)

References 461(4)
About the Editors 465(2)
List of Contributors 467(6)
Index 473