Microscopic Image Analysis for Life Science Applications

Name: Microscopic Image Analysis for Life Science Applications
Brand: Artech House
Price: 158.99 EUR
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Jens Rittscher(Editor)

Artech House (Publisher)

1st Edition

Published on 1. January 2008

432 pages

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978-1-59693-237-1 (ISBN)

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Microscopic Image Analysis for LifeScience Applications
Contents
Foreword
Preface
Chapter 1 Introduction to Biological Light Microscopy
1.1 Introduction
1.2 Need for Microscopy
1.3 Image Formation in Transmitted Light Microscopy
1.4 Resolution, Magnification, and Contrast in Microscopy
1.5 Phase Contrast Microscopy
1.6 Dark Field Microscopy
1.7 Polarization Microscopy
1.8 Differential Interference Contrast Microscopy
1.9 Reflected Light Microscopy
1.10 Fluorescence Microscopy
1.11 Light Microscopy in Biology
1.12 Noise and Artifacts in Microscopic Images
1.13 Trends in Light Microscopy
References
Chapter 2 Molecular Probes for Fluorescence Microscopy
2.1 Introduction
2.2 Basic Characteristics of Fluorophores
2.3 Traditional Fluorescent Dyes
2.4 Alexa Fluor Dyes
2.5 Cyanine Dyes
2.6 Fluorescent Environmental Probes
2.7 Organelle Probes
2.8 Quantum Dots
2.9 Fluorescent Proteins
2.10 Hybrid Systems
2.11 Quenching and Photobleaching
2.12 Conclusions
References
Selected Bibliography
Chapter 3 Overview of Image Analysis Tools and Tasks for Microscopy
3.1 Image Analysis Framework
3.1.1 Continuous-Domain Image Processing
3.1.2 A/D Conversion
3.1.3 Discrete-Domain Image Processing
3.2 Image Analysis Tools for Microscopy
3.2.1 Signal and Image Representations
3.2.2 Fourier Analysis
3.2.3 Gabor Analysis
3.2.4 Multiresolution Analysis
3.2.5 Unsupervised, Data-Driven Representation and Analysis Methods
3.2.6 Statistical Estimation
3.3 Imaging Tasks in Microscopy
3.3.1 Intelligent Acquisition
3.3.2 Deconvolution, Denoising, and Restoration
3.3.3 Registration and Mosaicking
3.3.4 Segmentation, Tracing, and Tracking
3.3.5 Classification and Clustering
3.3.6 Modeling
3.4 Conclusions
References
Chapter 4 An Introduction to Fluorescence Microscopy: Basic Principles, Challenges, and Opportunities
4.1 Fluorescence in Molecular and Cellular Biology
4.1.1 The Physical Principles of Fluorescence
4.1.2 The Green Revolution
4.2 Microscopes and Image Formation
4.2.1 The Widefield Microscope
4.2.2 The Confocal Scanning Microscope
4.2.3 Sample Setup and Aberrations
4.3 Detectors
4.3.1 Characteristic Parameters of Detection Systems
4.3.2 Detection Technologies
4.4 Limiting Factors of Fluorescence Imaging
4.4.1 Noise Sources
4.4.2 Sample-Dependent Limitations
4.5 Advanced Experimental Techniques
4.5.1 FRET
4.5.2 FRAP
4.5.3 FLIM
4.6 Signal and Image Processing Challenges
4.6.1 Data Size and Dimensionality
4.6.2 Image Preparation
4.6.3 Restoration
4.6.4 Registration
4.6.5 Segmentation
4.6.6 Quantitative Analysis
4.7 Current and Future Trends
4.7.1 Fluorescent Labels
4.7.2 Advanced Microscopy Systems
4.7.3 Super-Resolution: Photoactivated Localization-Based Techniques
4.8 Conclusions
References
Chapter 5 FARSIGHT: A Divide and Conquer Methodology for Analyzing Complex and Dynamic Biological Microenvironments
5.1 Introduction
5.2 A Divide-and-Conquer Segmentation Strategy
5.3 Computing and Representing Image-Based Measurements
5.4 Analysis of Spatio-Temporal Associations
5.5 Validation of Automated Image Analysis Results
5.6 Summary, Discussion, and Future Directions
References
Chapter 6 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
6.1 Introduction
6.2 MST
6.3 MST-Cut for Clustering of Coexpressed/Coregulated Genes
6.3.1 MST-Cut Clustering Algorithm
6.3.2 Embryonic Image Clustering
6.4 Experiments
6.4.1 Performance of MST-Cut on Synthetic Datasets
6.4.2 Detection of Coregulated Genes and Regulatory Motifs
6.5 Conclusions
Acknowledgments
References
Selected Bibliography
Chapter 7 Simulation and Estimation of Intracellular Dynamics and Trafficking
7.1 Context
7.1.1 Introduction to Intracellular Traffic
7.1.2 Introduction to Living Cell Microscopy
7.2 Modeling and Simulation Framework
7.2.1 Intracellular Trafficking Models in Video-Microscopy
7.2.2 Intracellular Traffic Simulation
7.2.3 Example
7.3 Background Estimation in Video-Microscopy
7.3.1 Pixel-Wise Estimation
7.3.2 Spatial Coherence for Background Estimation
7.3.3 Example
7.4 Foreground Analysis: Network Tomography
7.4.1 Network Tomography Principle
7.4.2 Measurements
7.4.3 Problem Optimization
7.4.4 Experiments
7.5 Conclusions
References
Chapter 8 Techniques for Cellular and Tissue-Based Image Quantitation of Protein Biomarkers
8.1 Current Methods for Histological and Tissue-Based Biomarker
8.2 Multiplexing
8.2.1 Fluorescence Microscopy
8.2.2 Fluorescent Dyes
8.2.3 Quantum Dots
8.2.4 Photobleaching
8.3 Image Analysis
8.3.1 Image Preprocessing
8.3.2 Image Registration
8.3.3 Image Segmentation
8.3.4 A Unified Segmentation Algorithm
8.3.5 Segmentation of Cytoplasm and Epithelial Regions
8.4 Multichannel Segmentation Techniques
8.5 Quantitation of Subcellular Biomarkers
8.6 Summary
Acknowledgments
References
Chapter 9 Methods for High-Content, High-Throughput, Image-Based Cell Screening
9.1 Introduction
9.2 Challenges in Image-Based High-Content Screening
9.3 Methods
9.3.1 Illumination and Staining Correction
9.3.2 Segmentation
9.3.3 Measurements
9.3.4 Spatial Bias Correction
9.3.5 Exploration and Inference
9.4 Discussion
Acknowledgments
References
Chapter 10 Particle Tracking in 3D+t Biological Imaging
10.1 Introduction
10.2 Main Tracking Methods
10.2.1 Autocorrelation Methods
10.2.2 Deterministic Methods
10.2.3 Multiple Particle Tracking Methods
10.2.4 Bayesian Methods
10.3 Analysis of Bayesian Filters
10.3.1 The Conceptual Filter
10.3.2 The Kalman Filter
10.3.3 The Filter Based on a Grid
10.3.4 The Extended Kalman Filter
10.3.5 The Interacting Multiple Model Filter
10.3.6 The Approximated Filter Based on a Grid
10.3.7 The Particle Filter
10.4 Description of the Main Association Methods
10.4.1 The Nearest Neighbor (ML)
10.4.2 Multihypothesis Tracking (MHT)
10.4.3 The Probabilistic Data Association Filter (PDAF)
10.4.4 Joint PDAF (JPDAF)
10.5 Particle Tracking: Methods for Biological Imaging
10.5.1 Proposed Dynamic Models for the IMM
10.5.2 Adaptive Validation Gate
10.5.3 Association
10.6 Applications
10.6.1 Validation on Synthetic Data
10.7 Conclusions
References
Appendix 10A Pseudocodes for the Algorithms
Chapter 11 Automated Analysis of the Mitotic Phases of Human Cells in 3-DFluorescence Microscopy Image Sequences
11.1 Introduction
11.2 Methods
11.2.1 Image Analysis Workflow
11.2.2 Segmentation of Multicell Images
11.2.3 Tracking of Mitotic Cell Nuclei
11.2.4 Extraction of Static and Dynamic Features
11.2.5 Classification
11.3 Experimental Results
11.3.1 Image Data
11.3.2 Classification Results
11.4 Discussion and Conclusion
Acknowledgments
References
Chapter 12 Automated Spatio-Temporal CellCycle Phase Analysis Based on Covert GFP Sensors
12.1 Introduction
12.2 Biological Background
12.2.1 Cell Cycle Phases
12.2.2 Cell Cycle Checkpoints
12.2.3 Cell Staining
12.2.4 Problem Statement
12.3 State of the Art
12.4 Mathematical Framework: Level Sets
12.4.1 Active Contours with Edges
12.5 Spatio-Temporal Cell Cycle Phase Analysis
12.5.1 Automatic Seed Placement
12.5.2 Shape/Size Constraint for Level Set Segmentation
12.5.3 Model-Based Fast Marching Cell Phase Tracking
12.6 Results
12.6.1 Large-Scale Toxicological Study
12.6.2 Algorithmic Validation
12.7 A Tool for Cell Cycle Research
12.7.1 Research Prototype
12.7.2 Visualization
12.8 Summary and Future Work
References
Chapter 13 Cell Segmentation for Division Rate Estimation in Computerized Video Time-Lapse Microscopy
13.1 Introduction
13.2 Methodology
13.2.1 Cell Detection with AdaBoost
13.2.2 Foreground Segmentation
13.2.3 Cytoplasm Segmentation Using the Watershed Algorithm
13.2.4 Cell Division Rate Estimation
13.3 Experiments
13.4 Conclusions
References
Systems Biology and the Digital Fish Project: A Vast New Frontier for Image Analysis
14.1 Introduction
14.2 Imaging-Based Systems Biology
14.2.1 What Is Systems Biology?
14.2.2 Imaging in Systems Biology
14.3 Example: The Digital Fish Project
14.3.1 Goals of Project
14.3.2 Why Fish?
14.3.3 Imaging
14.3.4 Image Analysis
14.3.5 Visualization
14.3.6 Data Analysis
14.3.7 Registration/Integration, Reference Atlas
14.4 Bridging the Gap
14.4.1 Open Source
14.4.2 Traversing the Gap
14.5 Conclusions
Acknowledgments
References
Chapter 15 Quantitative Phenotyping Using Microscopic Images
15.1 Introduction
15.2 Relevant Biomedical Applications
15.2.1 Mouse Model Phenotyping Study: Role of the Rb Gene
15.2.2 Mouse Model Phenotyping Study: The PTEN Gene and Cancer
15.2.3 3-D Reconstruction of Cellular Structure of Zebrafish Embryo
15.3 Tissue Segmentation Using N-Point Correlation Functions
15.3.1 Introduction to N-Point Correlation Functions
15.3.2 Segmentation of Microscopic Images Using N-pcfs
15.4 Segmentation of Individual Cells
15.4.1 Modality-Dependent Segmentation: Active Contour Models
15.4.2 Modality-Independent Segmentation: Using Tessellations
15.5 Registration of Large Microscopic Images
15.5.1 Rigid Registration
15.5.2 Nonrigid Registration
15.6 3-D Visualization
15.6.1 Mouse Model Phenotyping Study: Role of the Rb Gene
15.6.2 Mouse Model Phenotyping Study: Role of the PTEN Gene in Cancer
15.6.3 Zebrafish Phenotyping Studies
15.7 Quantitative Validation
15.7.1 Mouse Placenta Phenotyping Studies
15.7.2 Mouse Mammary Gland Phenotyping Study
15.8 Summary
References
Chapter 16 Automatic 3-D Morphological Reconstruction of Neuron Cells from Multiphoton Images
16.1 Introduction
16.2 Materials and Methods
16.2.1 Experimental Data
16.3 Results
16.4 Conclusions
Acknowledgments
References
Chapter 17 Robust 3-D Reconstruction and Identification of Dendritic Spines
17.1 Introduction
17.2 Related Work
17.3 Image Acquisition and Processing
17.3.1 Data-Set
17.3.2 Denoising and Resampling
17.3.3 Segmenting the Neuron
17.3.4 Floating Spine Heads
17.4 Neuron Reconstruction and Analysis
17.4.1 Surfacing and Surface Fairing
17.4.2 Curve Skeletonization
17.4.3 Dendrite Tree Model
17.4.4 Morphometry and Spine Identification
17.5 Results
17.6 Conclusion
Acknowledgments
References
Chapter 18 Small Critter Imaging
18.1 In Vivo Molecular Small Animal Imaging
18.1.1 Fluorescence Microscopic Imaging
18.1.2 Bioluminescence Imaging
18.1.3 Coherent Anti-Stokes Raman Scattering Imaging
18.1.4 Fibered In Vivo Imaging
18.2 Fluorescence Molecular Imaging (FMT)
18.2.1 Fluorescence Scanning
18.2.2 FMT Data Processing
18.2.3 Multimodality
18.3 Registration of 3-D FMT and MicroCT Images
18.3.1 Introduction
18.3.2 Problem Statement and Formulation
18.3.3 Combined Differential Evolution and Simplex Method Optimization
18.3.4 A Novel Optimization Method Based on Sequential Monte Carlo
18.4 Conclusions
Acknowledgments
References
Chapter 19 Processing of In Vivo Fibered Confocal Microscopy Video Sequences
19.1 Motivations
19.2 Principles of Fibered Confocal Microscopy
19.2.1 Confocal Microscopy
19.2.2 Distal Scanning Fibered Confocal Microscopy
19.2.3 Proximal Scanning Fibered Confocal Microscopy
19.3 Real-Time Fiber Pattern Rejection
19.3.1 Calibrated Raw Data Acquisition
19.3.2 Real-Time Processing
19.4 Blood Flow Velocimetry Using Motion Artifacts
19.4.1 Imaging of Moving Objects
19.4.2 Velocimetry Algorithm
19.4.3 Results and Evaluation
19.5 Region Tracking for Kinetic Analysis
19.5.1 Motion Compensation Algorithm
19.5.2 Affine Registration Algorithm
19.5.3 Application to Cell Trafficking
19.6 Mosaicking: Bridging the Gap Between Microscopic and Macroscopic Scales
19.6.1 Overview of the Algorithm
19.6.2 Results and Evaluation
19.7 Conclusions
References
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