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High-Throughput Image Reconstruction and Analysis Unabridged edition [Kietas viršelis]

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  • Formatas: Hardback, 380 pages
  • Išleidimo metai: 31-Dec-2008
  • Leidėjas: Artech House Publishers
  • ISBN-10: 1596932953
  • ISBN-13: 9781596932951
Kitos knygos pagal šią temą:
High-Throughput Image Reconstruction and Analysis Unabridged editionDidesnis paveikslėlis
  • Formatas: Hardback, 380 pages
  • Išleidimo metai: 31-Dec-2008
  • Leidėjas: Artech House Publishers
  • ISBN-10: 1596932953
  • ISBN-13: 9781596932951
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781596932951.html
Raktažodžiai:
Today's bioimaging technologies generate mountains of biological data that can simply overwhelm conventional analysis methods. This groundbreaking book helps researchers blast through the computational bottleneck with high-performance computing (HPC) techniques that are blazing the way to never-before bioimaging, image analysis, and data mining capabilities and revolutionizing the study of cellular and organ-level systems. This innovative volume surveys the latest advances in scanning electron microscopy, knife-edge scanning microscopy, and 4D imaging of multi-component biological systems. The book includes detailed case studies that show how key techniques have been successfully deployed in medical image analysis, drug discovery, tissue analysis, functional MRI data analysis, and other areas.
Introduction
1(8)
Part I: Emerging Technologies to Understand Biological Systems
3(1)
Knife-Edge Scanning Microscopy: High-Throughput Imaging and Analysis of Massive Volumes of Biological Microstructures
3(1)
4D Imaging of Multicomponent Biological Systems
3(1)
Utilizing Parallel Processing in Computational Biology Applications
3(1)
Part II: Understanding and Utilizing Parallel Processing Techniques
4(1)
Introduction to High-Performance Computing Using MPI and OpenMP
4(1)
Parallel Feature Extraction
4(1)
Machine Learning Techniques for Large Data
4(1)
Part III: Specific Applications of Parallel Computing
5(2)
Scalable Image Registration and 3D Reconstruction at Microscopic Resolution
5(1)
Data Analysis Pipeline for High-Content Screening in Drug Discovery
5(1)
Information About Color and Orientation in the Primate Visual Cortex
5(1)
High-Throughput Analysis of Microdissected Tissue Samples
6(1)
Applications of High-Performance Computing to Functional Magnetic Resonance Imaging (fMRI) Data
6(1)
Part IV: Postprocessing
7(1)
Bisque: A Scalable Biological Image Database and Analysis Framework
7(1)
High-Performance Computing Applications for Visualization of Large Microscopy Images
7(1)
Conclusion
7(2)
Acknowledgments
8(1)
PART I Emerging Technologies to Understand Biological Systems
9(94)
Knife-Edge Scanning Microscopy: High-Throughput Imaging and Analysis of Massive Volumes of Biological Microstructures
11(28)
Background
11(5)
High-Throughput, Physical-Sectioning Imaging
11(3)
Volumetric Data Analysis Methods
14(2)
Knife-Edge Scanning Microscopy
16(5)
Tracing in 2D
21(4)
Tracing in 3D
25(4)
Interactive Visualization
29(2)
Discussion
31(3)
Validation and Editing
32(1)
Exploiting Parallelism
33(1)
Conclusion
34(5)
Acknowledgments
34(1)
References
34(5)
Parallel Processing Strategies for Cell Motility and Shape Analysis
39(48)
Cell Detection
39(5)
Flux Tensor Framework
39(3)
Flux Tensor Implementation
42(2)
Cell Segmentation Using Level Set-Based Active Contours
44(24)
Region-Based Active Contour Cell Segmentation
45(7)
Edge-Based Active Contour Cell Segmentation
52(3)
GPU Implementation of Level Sets
55(10)
Results and Discussion
65(3)
Cell Tracking
68(19)
Cell-to-Cell Temporal Correspondence Analysis
69(4)
Trajectory Segment Generation
73(1)
Distributed Cell Tracking on Cluster of Workstations
74(3)
Results and Discussion
77(3)
References
80(7)
Utilizing Parallel Processing in Computational Biology Applications
87(16)
Introduction
87(1)
Algorithms
88(4)
Tumor Cell Migration
89(1)
Tissue Environment
90(1)
Processes Controlling Individual Tumor Cells
90(1)
Boundary Conditions
91(1)
Nondimensionalization and Parameters
92(1)
Model Simulation
92(1)
Decomposition
92(5)
Moving of Tumor Cells
94(1)
Copying of Tumor Cells
95(1)
Copying of Continuous Variables
95(1)
Blue Gene Model Simulation
96(1)
Multithreaded Blue Gene Model Simulation
96(1)
Performance
97(2)
Conclusions
99(4)
Acknowledgments
100(1)
References
100(3)
PART II Understanding and Utilizing Parallel Processing Techniques
103(76)
Introduction to High-Performance Computing using MPI
105(38)
Introduction
105(3)
Parallel Architectures
108(3)
Parallel Programming Models
111(3)
The Three P's of a Parallel Programming Model
112(2)
The Message Passing Interface
114(21)
The Nine Basic Functions to Get Started with MPI Programming
115(17)
Other MPI Features
132(3)
Other Programming Models
135(4)
Conclusions
139(4)
References
140(3)
Parallel Feature Extraction
143(18)
Introduction
143(1)
Background
143(2)
Serial Block-Face Scanning
144(1)
Computational Methods
145(3)
3D Filtering
145(1)
3D Connected Component Analysis
145(1)
Mathematical Morphological Operators
146(1)
Contour Extraction
146(1)
Requirements
147(1)
Parallelization
148(4)
Computation Issues
148(1)
Communication Issues
148(1)
Memory and Storage Issues
149(1)
Domain Decomposition for Filtering Tasks
149(2)
Domain Decomposition for Morphological Operators
151(1)
Domain Decomposition for Contour Extraction Tasks
151(1)
Computational Results
152(5)
Median Filtering
152(1)
Contour Extraction
153(3)
Related Work
156(1)
Conclusion
157(4)
References
158(3)
Machine Learning Techniques for Large Data
161(18)
Introduction
161(1)
Feature Reduction and Feature Selection Algorithms
162(2)
Clustering Algorithms
164(2)
Classification Algorithms
166(7)
Material Not Covered in This
Chapter
173(6)
References
173(6)
PART III Specific Applications of Parallel Computing
179(104)
Scalable Image Registration and 3D Reconstruction at Microscopic Resolution
181(28)
Introduction
181(2)
Review of Large-Scale Image Registration
183(2)
Common Approaches for Image Registration
183(1)
Registering Microscopic Images for 3D Reconstruction in Biomedical Research
184(1)
HPC Solutions for Image Registration
185(1)
Two-Stage Scalable Registration Pipeline
185(8)
Fast Rigid Initialization
185(3)
Nonrigid Registration
188(3)
Image Transformation
191(1)
3D Reconstruction
192(1)
High-Performance Implementation
193(4)
Hardware Arrangement
193(1)
Workflow
193(3)
GPU Acceleration
196(1)
Experimental Setup
197(1)
Benchmark Dataset and Parameters
197(1)
The Multiprocessor System
198(1)
Experimental Results
198(6)
Visual Results
198(1)
Performance Results
199(5)
Summary
204(5)
References
205(4)
Data Analysis Pipeline for High Content Screening in Drug Discovery
209(20)
Introduction
209(1)
Background
209(1)
Types of HCS Assay
210(2)
HCS Sample Preparation
212(1)
Cell Culture
212(1)
Staining
212(1)
Image Acquisition
212(2)
Image Analysis
214(1)
Data Analysis
215(8)
Data Process Pipeline
215(1)
Preprocessing Normalization Module
216(2)
Dose Response and Confidence Estimation Module
218(1)
Automated Cytometry Classification Module
219(4)
Factor Analysis
223(3)
Conclusion and Future Perspectives
226(3)
Acknowledgments
226(1)
References
226(3)
Information About Color and Orientation in the Primate Visual Cortex
229(12)
Introduction
229(4)
Monitoring Activity in Neuronal Populations: Optical Imaging and Other Methods
230(3)
Methods and Results
233(3)
Discussion
236(5)
Acknowledgments
238(1)
References
238(3)
High-Throughput Analysis of Microdissected Tissue Samples
241(22)
Introduction
241(1)
Microdissection Techniques and Molecular Analysis of Tissues
242(5)
General Considerations
242(1)
Fixation---A Major Consideration When Working with Tissue Samples
242(1)
Why Is Microdissection Important When Using Tissue Samples?
243(1)
Tissue Microdissection Techniques
243(4)
DNA Analysis of Microdissected Samples
247(2)
General Considerations
247(1)
Loss of Heterozygosity (LOH)
247(1)
Global Genomic Amplification
248(1)
Epigenetic Analysis
248(1)
Mitochondrial DNA Analysis
248(1)
mRNA Analysis of Microdissected Samples
249(1)
General Considerations
249(1)
Expression Microarrays
249(1)
Quantitative RT-PCR
249(1)
Protein Analysis of Microdissected Samples
250(3)
General Considerations
250(1)
Western Blot
250(1)
Two-Dimensional Polyacrylamide Gel Electrophoresis (2D-PAGE)
251(1)
Mass Spectrometry
252(1)
Protein Arrays
252(1)
Statistical Analysis of Microdissected Samples
253(3)
General Considerations
253(1)
Quantification of Gene Expression
253(1)
Sources of Variation When Studying Microdissected Material
254(1)
Comparisons of Gene Expression Between Two Groups
254(1)
Microarray Analysis
255(1)
Conclusions
256(7)
References
256(7)
Applications of High-Performance Computing to Functional Magnetic Resonance Imaging (fMRI) Data
263(20)
Introduction
263(1)
fMRI Image Analysis Using the General Linear Model (GLM)
263(1)
fMRI Image Analysis Based on Connectivity
264(1)
The Theory of Granger Causality
264(4)
The Linear Simplification
265(2)
Sparse Regression
267(1)
Solving Multivariate Autoregressive Model Using Lasso
267(1)
Implementing Granger Causality Analysis on the Blue Gene/L Supercomputer
268(6)
A Brief Overview of the Blue Gene/L Supercomputer
269(1)
MATLAB on Blue Gene/L
270(1)
Parallelizing Granger Causality Analysis
270(4)
Experimental Results
274(5)
Simulations
274(1)
Simulation Setup
274(1)
Results
275(2)
Analysis of fMRI Data
277(2)
Discussion
279(4)
References
280(3)
PART IV Postprocessing
283(38)
Bisque: A Scalable Biological Image Database and Analysis Framework
285(18)
Introduction
285(1)
Datasets and Domain Needs
285(1)
Large-Scale Image Analysis
285(1)
State of the Art: PSLID, OME, and OMERO
286(1)
Rationale for Bisque
286(3)
Image Analysis
288(1)
Indexing Large Image Collections
288(1)
Design of Bisque
289(9)
DoughDB: A Tag-Oriented Database
289(3)
Integration of Information Resources
292(1)
Distributed Architecture for Scalable Computing
293(4)
Analysis Framework
297(1)
Analysis Architectures for Future Applications
298(2)
Concluding Remarks
300(3)
References
300(3)
High-Performance Computing Applications for Visualization of Large Microscopy Images
303(18)
Mesoscale Problem: The Motivation
303(2)
High-Performance Computing for Visualization
305(5)
Data Acquisition
306(1)
Computation
306(1)
Data Storage and Management
307(1)
Moving Large Data with Optical Networks
307(1)
Challenges of Visualizing Large Data Interactively
308(2)
Visualizing Large 2D Image Data
310(1)
Visualizing Large 3D Volume Data
311(3)
Management of Scalable High-Resolution Displays
314(2)
SAGE (Scalable Adaptive Graphics Environment)
314(1)
COVISE (Collaborative Visualization and Simulation Environment)
315(1)
Virtual Reality Environments
316(2)
CAVE (Cave Automatic Virtual Environment)
316(2)
Varrier
318(1)
Future of Large Data Visualization
318(1)
Conclusion
318(3)
References
319(2)
About the Editors 321(2)
List of Contributors 323(4)
Index 327
A. Ravishankar Rao is a research staff member with the Biometaphorical Computing Group at the IBM T.J. Watson Research Center, Yorktown Heights, New York. He is also an associate editor of the journals Pattern Recognition and Machine Vision and Applications. He was named a Master Inventor at IBM Research in 2004. Guillermo A. Cecchi is a research staff member with the Biometaphorical Computing Group at the IBM T.J. Watson Research Center, Yorktown Heights, New York. He also served as a postdoctoral fellow at Cornell University.