High-Throughput Image Reconstruction and Analysis

Name: High-Throughput Image Reconstruction and Analysis
Brand: Artech House
Price: 129.99 EUR
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A. Ravishankar Rao(Author)

Artech House (Publisher)

1st Edition

Published on 1. January 2008

380 pages

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978-1-59693-296-8 (ISBN)

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Content

Intro
Contents
Chapter 1: Introduction
1.1 Part I: Emerging Technologies to Understand Biological Systems
1.1.1 Knife-Edge Scanning Microscopy: High-Throughput Imaging and Analysis of Massive Volumes of Biological Microstructures
1.1.2 4D Imaging of Multicomponent Biological Systems
1.1.3 Utilizing Parallel Processing in Computational Biology Applications
1.2 Part II: Understanding and Utilizing Parallel Processing Techniques
1.2.1 Introduction to High-Performance Computing Using MPI and OpenMP
1.2.2 Parallel Feature Extraction
1.2.3 Machine Learning Techniques for Large Data
1.3 Part III: Specific Applications of Parallel Computing
1.3.1 Scalable Image Registration and 3D Reconstruction at Microscopic Resolution
1.3.2 Data Analysis Pipeline for High-Content Screening in Drug Discovery
1.3.3 Information About Color and Orientation in the Primate Visual Cortex
1.3.4 High-Throughput Analysis of Microdissected Tissue Samples
1.3.5 Applications of High-Performance Computing to Functional Magnetic Resonance Imaging (fMRI) Data
1.4 Part IV: Postprocessing
1.4.1 Bisque: A Scalable Biological Image Database and Analysis Framework
1.4.2 High-Performance Computing Applications for Visualization of Large Microscopy Images
1.5 Conclusion
Acknowledgments
Part I: Emerging Technologies to Understand Biological Systems
Chapter 2: Knife-Edge Scanning Microscopy:High-Throughput Imaging and Analysis of Massive Volumes of Biological Microstructures
2.1 Background
2.1.1 High-Throughput, Physical-Sectioning Imaging
2.1.2 Volumetric Data Analysis Methods
2.2 Knife-Edge Scanning Microscopy
2.3 Tracing in 2D
2.4 Tracing in 3D
2.5 Interactive Visualization
2.6 Discussion
2.6.1 Validation and Editing
2.6.2 Exploiting Parallelism
2.7 Conclusion
Acknowledgments
References
Chapter 3: Parallel Processing Strategies for Cell Motility and Shape Analysis
3.1 Cell Detection
3.1.1 Flux Tensor Framework
3.1.2 Flux Tensor Implementation
3.2 Cell Segmentation Using Level Set-Based Active Contours
3.2.1 Region-Based Active Contour Cell Segmentation
3.2.2 Edge-Based Active Contour Cell Segmentation
3.2.3 GPU Implementation of Level Sets
3.2.4 Results and Discussion
3.3 Cell Tracking
3.3.1 Cell-to-Cell Temporal Correspondence Analysis
3.3.2 Trajectory Segment Generation
3.3.3 Distributed Cell Tracking on Cluster of Workstations
3.3.4 Results and Discussion
References
Chapter 4: Utilizing Parallel Processing in Computational Biology Applications
4.1 Introduction
4.2 Algorithms
4.2.1 Tumor Cell Migration
4.2.2 Tissue Environment
4.2.3 Processes Controlling Individual Tumor Cells
4.2.4 Boundary Conditions
4.2.5 Nondimensionalization and Parameters
4.2.6 Model Simulation
4.3 Decomposition
4.3.1 Moving of Tumor Cells
4.3.2 Copying of Tumor Cells
4.3.3 Copying of Continuous Variables
4.3.4 Blue Gene Model Simulation
4.3.5 Multithreaded Blue Gene Model Simulation
4.4 Performance
4.5 Conclusions
Acknowledgments
References
Part II: Understanding and Utilizing Parallel Processing Techniques
Chapter 5: Introduction to High-Performance Computing Using MPI
5.1 Introduction
5.2 Parallel Architectures
5.3 Parallel Programming Models
5.3.1 The Three P's of a Parallel Programming Model
5.4 The Message Passing Interface
5.4.1 The Nine Basic Functions to Get Started with MPI Programming
5.4.2 Other MPI Features
5.5 Other Programming Models
5.6 Conclusions
References
Chapter 6: Parallel Feature Extraction
6.1 Introduction
6.2 Background
6.2.1 Serial Block-Face Scanning
6.3 Computational Methods
6.3.1 3D Filtering
6.3.2 3D Connected Component Analysis
6.3.3 Mathematical Morphological Operators
6.3.4 Contour Extraction
6.3.5 Requirements
6.4 Parallelization
6.4.1 Computation Issues
6.4.2 Communication Issues
6.4.3 Memory and Storage Issues
6.4.4 Domain Decomposition for Filtering Tasks
6.4.5 Domain Decomposition for Morphological Operators
6.4.6 Domain Decomposition for Contour Extraction Tasks
6.5 Computational Results
6.5.1 Median Filtering
6.5.3 Related Work
6.5.2 Contour Extraction
6.6 Conclusion
References
Chapter 7: Machine Learning Techniques for Large Data
7.1 Introduction
7.2 Feature Reduction and Feature Selection Algorithms
7.3 Clustering Algorithms
7.4 Classification Algorithms
7.5 Material Not Covered in This Chapter
References
Part III: Specific Applications of Parallel Computing
Chapter 8: Scalable Image Registration and 3D Reconstruction at Microscopic Resolution
8.1 Introduction
8.2 Review of Large-Scale Image Registration
8.2.1 Common Approaches for Image Registration
8.2.2 Registering Microscopic Images for 3D Reconstruction in Biomedical Research
8.2.3 HPC Solutions for Image Registration
8.3 Two-Stage Scalable Registration Pipeline
8.3.1 Fast Rigid Initialization
8.3.2 Nonrigid Registration
8.3.3 Image Transformation
8.3.4 3D Reconstruction
8.4 High-Performance Implementation
8.4.1 Hardware Arrangement
8.4.2 Workflow
8.4.3 GPU Acceleration
8.5 Experimental Setup
8.5.1 Benchmark Dataset and Parameters
8.5.2 The Multiprocessor System
8.6 Experimental Results
8.6.1 Visual Results
8.6.2 Performance Results
8.7 Summary
References
Chapter 9: Data Analysis Pipeline for High Content Screening in Drug Discovery
9.1 Introduction
9.2 Background
9.3 Types of HCS Assay
9.4 HCS Sample Preparation
9.4.1 Cell Culture
9.4.2 Staining
9.5 Image Acquisition
9.6 Image Analysis
9.7 Data Analysis
9.7.1 Data Process Pipeline
9.7.2 Preprocessing Normalization Module
9.7.3 Dose Response and Confidence Estimation Module
9.7.4 Automated Cytometry Classification Module
9.8 Factor Analysis
9.9 Conclusion and Future Perspectives
Acknowledgments
References
Chapter 10: Information About Color and Orientation in the Primate Visual Cortex
10.1 Introduction
10.1.1 Monitoring Activity in Neuronal Populations: Optical Imaging and Other Methods
10.2 Methods and Results
10.3 Discussion
Acknowledgments
References
Chapter 11: High-Throughput Analysis of Microdissected Tissue Samples
11.1 Introduction
11.2 Microdissection Techniques and Molecular Analysis of Tissues
11.2.1 General Considerations
11.2.2 Fixation----A Major Consideration When Working with Tissue Samples
11.2.3 Why Is Microdissection Important When Using Tissue Samples?
11.2.4 Tissue Microdissection Techniques
11.3 DNA Analysis of Microdissected Samples
11.3.1 General Considerations
11.3.2 Loss of Heterozygosity (LOH)
11.3.3 Global Genomic Amplification
11.3.4 Epigenetic Analysis
11.3.5 Mitochondrial DNA Analysis
11.4 mRNA Analysis of Microdissected Samples
11.4.1 General Considerations
11.4.2 Expression Microarrays
11.4.3 Quantitative RT-PCR
11.5 Protein Analysis of Microdissected Samples
11.5.1 General Considerations
11.5.2 Western Blot
11.5.3 Two-Dimensional Polyacrylamide Gel Electrophoresis (2D-PAGE)
11.5.4 Mass Spectrometry
11.5.5 Protein Arrays
11.6 Statistical Analysis of Microdissected Samples
11.6.1 General Considerations
11.6.2 Quantification of Gene Expression
11.6.3 Sources of Variation When Studying Microdissected Material
11.6.4 Comparisons of Gene Expression Between Two Groups
11.6.5 Microarray Analysis
11.7 Conclusions
References
Chapter 12: Applications of High-Performance Computing to Functional Magnetic Resonance Imaging (fMRI) Data
12.1 Introduction
12.1.1 fMRI Image Analysis Using the General Linear Model (GLM)
12.1.2 fMRI Image Analysis Based on Connectivity
12.2 The Theory of Granger Causality
12.2.1 The Linear Simplification
12.2.2 Sparse Regression
12.2.3 Solving Multivariate Autoregressive Model Using Lasso
12.3 Implementing Granger Causality Analysis on the Blue Gene/L Supercomputer
12.3.1 A Brief Overview of the Blue Gene/L Supercomputer
12.3.2 MATLAB on Blue Gene/L
12.3.3 Parallelizing Granger Causality Analysis
12.4 Experimental Results
12.4.1 Simulations
12.4.2 Simulation Setup
12.4.3 Results
12.4.4 Analysis of fMRI Data
12.5 Discussion
References
Part IV: Postprocessing
Chapter 13: Bisque: A Scalable Biological Image Database and Analysis Framework
13.1 Introduction
13.1.1 Datasets and Domain Needs
13.1.2 Large-Scale Image Analysis
13.1.3 State of the Art: PSLID, OME, and OMERO
13.2 Rationale for Bisque
13.2.1 Image Analysis
13.2.2 Indexing Large Image Collections
13.3 Design of Bisque
13.3.1 DoughDB: A Tag-Oriented Database
13.3.2 Integration of Information Resources
13.3.3 Distributed Architecture for Scalable Computing
13.3.4 Analysis Framework
13.4 Analysis Architectures for Future Applications
13.5 Concluding Remarks
References
Chapter 14: High-Performance Computing Applications for Visualization of Large Microscopy Images
14.1 Mesoscale Problem: The Motivation
14.2 High-Performance Computing for Visualization
14.2.1 Data Acquisition
14.2.2 Computation
14.2.3 Data Storage and Management
14.2.4 Moving Large Data with Optical Networks
14.2.5 Challenges of Visualizing Large Data Interactively
14.3 Visualizing Large 2D Image Data
14.4 Visualizing Large 3D Volume Data
14.5 Management of Scalable High-Resolution Displays
14.5.1 SAGE (Scalable Adaptive Graphics Environment)
14.5.2 COVISE (Collaborative Visualization and Simulation Environment)
14.6 Virtual Reality Environments
14.6.1 CAVE (Cave Automatic Virtual Environment)
14.6.2 Varrier
14.7 Future of Large Data Visualization
14.8 Conclusion
References
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