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1.

図書

図書
edited by G. T. Herman and F. Natterer
出版情報: Berlin ; New York : Springer-Verlag, 1981  309 p. ; 25 cm
シリーズ名: Lecture notes in medical informatics ; 8
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2.

図書

図書
[by] Gabor T. Herman and Grzegorz Rozenberg, with a contribution by Aristid Lindenmayer
出版情報: Amsterdam : North-Holland Pub. Co. , New York : American Elsevier Pub. Co., 1975  xvi, 363 p. ; 23 cm
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3.

図書

図書
a set of lectures by G.T. Herman ... [et al.] ; foreword by R. Dautray ; edited by P.C. Sabatier
出版情報: Bristol ; Philadelphia : A. Hilger, c1987  xii, 671 p. ; 24 cm
シリーズ名: Malvern physics series
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4.

図書

図書
G.T. Herman, A.K. Louis, F. Natterer, (eds.)
出版情報: Berlin ; New York : Springer-Verlag, c1991  x, 268 p. ; 25 cm
シリーズ名: Lecture notes in mathematics ; 1497
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5.

電子ブック

EB
Gabor T. Herman, Gabor T. Herman, Karl Louis, Frank Natterer, Mathematisches Forschungsinstitut Oberwolfach.
出版情報: SpringerLink Books Lecture Notes In Mathematics Archive , Springer Berlin Heidelberg, 1991
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6.

電子ブック

EB
Gabor T. Herman
出版情報: Springer eBooks Computer Science , Springer London, 2009
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目次情報: 続きを見る
Introduction / 1:
Image Reconstruction from Projections / 1.1:
Probability and Random Variables / 1.2:
An Overview of the Process of CT / 2:
What Are We Trying to Do? / 2.1:
Traditional Tomography / 2.2:
Data Collection for CT / 2.3:
Voxels, Pixels, and CT Numbers / 2.4:
The Problem of Polychromaticity / 2.5:
Reconstruction Algorithms / 2.6:
Physical Problems Associated with Data Collection in CT / 3:
Photon Statistics / 3.1:
Beam Hardening / 3.2:
Other Sources of Error / 3.3:
Scanning Modes / 3.4:
Computer Simulation of Data Collection in CT / 4:
Pictures and Digitization / 4.1:
Creation of a Phantom / 4.2:
A Piecewise-Homogeneous Head Phantom / 4.3:
Head Phantom with a Large Tumor and Local Inhomogeneities / 4.4:
Creation of the Ray Sums / 4.5:
Fast Calculation of a Ray Sum for a Digitized Picture / 4.6:
Data Collection and Reconstruction of the Head Phantom / 5:
Methods of Picture Comparison / 5.1:
Task-Oriented Comparison of Algorithm Performance / 5.2:
An Illustration Using Selective Smoothing / 5.3:
Reconstruction from Perfect Data / 5.4:
Effects of Photons Statistics / 5.5:
Effect of Beam Hardening / 5.6:
The Effects of Detector Width and Scatter / 5.7:
Simulation of Different Scanning Modes / 5.8:
Basic Concepts of Reconstruction Algorithms / 6:
Problem Statement / 6.1:
Transform Methods / 6.2:
Series Expansion Methods / 6.3:
Optimization Criteria / 6.4:
Blob Basis Functions / 6.5:
Computational Efficiency / 6.6:
Backprojection / 7:
Continuous Backprojection / 7.1:
Implementation of the Backprojection Operator / 7.2:
Discrete Backprojection / 7.3:
Filtered Backprojection for Parallel Beams / 8:
Convolutions, Hilbert Transforms, Regularization / 8.1:
Derivation of the FBP Method / 8.2:
Implementation of the FBP Method / 8.3:
Fourier Transforms / 8.4:
Sampling and Interpolation
The Choice of Convolving and Interpolating Functions / 8.6:
Why So Popular? / 8.7:
Other Transform Methods for Parallel Beams / 9:
Two-Dimensional Fourier Transforms / 9.1:
The Fourier Method of Reconstruction / 9.2:
Linograms / 9.3:
Rho-Filtered Layergram / 9.4:
Filtered Backprojection for Divergent Beams / 10:
The Divergent Beam FBP Algorithm / 10.1:
Choice of the Window Function / 10.2:
Point Response Function / 10.3:
Noise Reconstruction / 10.4:
Comparison of Algorithms Based on Reconstructions / 10.5:
Algebraic Reconstruction Techniques / 11:
What is Art? / 11.1:
Relaxation Methods for Solving Systems of Inequalities and Equalities / 11.2:
Additive Art / 11.3:
Tricks / 11.4:
Efficacy of Art / 11.5:
Quadratic Optimization Methods / 12:
Mathematical Background to Quadratic Optimization / 12.1:
Richardson's Method for Solving Systems of Equations / 12.2:
Smoothing Matrices / 12.3:
Implementation of Richardson's Methods for Image Reconstruction / 12.4:
A Demonstration of Quadratic Optimization / 12.5:
Truly Three-Dimensional Reconstruction / 13:
Three-Dimensional Series Expansion / 13.1:
Dynamically Changing 3D Phantoms and Their Projections / 13.2:
Three-Dimensional Reconstructions of the Dynamic Phantom / 13.3:
Three-Dimensional Display of Organs / 14:
The Basic Approach / 14.1:
Boundary Detection / 14.2:
Hidden Surface Removal / 14.3:
Shading / 14.4:
Mathematical Background / 15:
The Dimensionality of the Linear Attenuation Coefficient / 15.1:
The Line Integral of the Relative Linear Attenuation / 15.2:
The Radon Inversion Formula / 15.3:
A Picture is Not Uniquely Determined by a Finite Number of Its Views / 15.4:
Analysis of the Photon Statistics / 15.5:
The Integral Expression for Polychromatic Ray Sums / 15.6:
Proof of the Regularization Theorem / 15.7:
Convergence of the Relaxation Method for Inequalities / 15.8:
References
Index
Introduction / 1:
Image Reconstruction from Projections / 1.1:
Probability and Random Variables / 1.2:
7.

図書

図書
Gabor T. Herman
出版情報: San Francisco : Academic Press, 1980  xiv, 316 p. ; 24 cm
シリーズ名: Computer science and applied mathematics
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8.

図書

図書
by Hans Hermes ; translated by G.T. Herman and O. Plassmann
出版情報: Berlin ; New York : Springer, 1965  ix, 245 p. ; 24 cm
シリーズ名: Die Grundlehren der mathematischen Wissenschaften ; Bd. 127
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9.

図書

図書
edited by Josef Raviv, associate editors, James F. Greenleaf and Gabor T. Herman
出版情報: Amsterdam ; New York : North-Holland Pub. Co. , New York : Sole distributors for the U.S.A. and Canada, Elsevier North-Holland, 1979  x, 319 p. ; 23 cm
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10.

図書

図書
edited by G. T. Herman ; with contributions by M. D. Altschuler ... [et al.]
出版情報: Berlin ; New York : Springer-Verlag, 1979  xii, 284 p. ; 24 cm
シリーズ名: Topics in applied physics ; v. 32
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目次情報: 続きを見る
Introduction / 1:
Why Learn R? / 1.1:
Is R Accurate? / 1.2:
What About Tech Support? / 1.3:
The Five Main Parts of SAS and SPSS / 2:
Programming Conventions / 3:
Typographic Conventions / 4:
Installing and Updating R / 5:
Installing Add-on Packages / 5.1:
Loading an Add-on Package / 5.2:
Updating Your Installation / 5.3:
Uninstalling R / 5.4:
Choosing Repositories / 5.5:
Accessing Data in Packages / 5.6:
Running R / 6:
Running R Interactively on Windows / 6.1:
Running R Interactively on Macintosh / 6.2:
Running R Interactively on Linux or UNIX / 6.3:
Running Programs that Include Other Programs / 6.4:
Running R in Batch Mode / 6.5:
Running R from SPSS / 6.6:
Graphical User Interfaces / 6.7:
R Commander / 6.7.1:
Rattle for Data Mining / 6.7.2:
JGR Java GUI for R / 6.7.3:
Help and Documentation / 7:
Help Files / 7.1:
Starting Help / 7.2:
Help Examples / 7.3:
Help for Functions that Call Other Functions / 7.4:
Help for Packages / 7.5:
Help for Datasets / 7.6:
Books and Manuals / 7.7:
E-mail Lists / 7.8:
Searching the Web / 7.9:
Vignettes / 7.10:
Programming Language Basics / 8:
Simple Calculations / 8.1:
Data Structures / 8.2:
Vectors / 8.2.1:
Factors / 8.2.2:
Data Frames / 8.2.3:
Matrices / 8.2.4:
Arrays / 8.2.5:
Lists / 8.2.6:
Saving Your Work So Far / 8.3:
Comments to Document Your Programs / 8.4:
Controlling Functions (Procedures) / 8.5:
Controlling Functions with Arguments / 8.5.1:
Controlling Functions with Formulas / 8.5.2:
Controlling Functions with an Object's Class / 8.5.3:
Controlling Functions with Extractor Functions - ODS, OMS / 8.5.4:
How Much Output Is There? / 8.5.5:
Writing Your Own Functions (Macros) / 8.5.6:
Data Acquisition / 9:
The R Data Editor / 9.1:
Reading Delimited Text Files / 9.2:
Reading Text Data Within a Program (Datalines, Cards, Begin Data...) / 9.3:
Reading Data from the Keyboard / 9.4:
Reading Fixed-Width Text Files, One Record per Case / 9.5:
Macro Substitution / 9.5.1:
Reading Fixed-Width Text Files, Two or More Records per Case / 9.6:
Importing Data from SAS / 9.7:
Importing Data from SPSS / 9.8:
Exporting Data / 9.9:
Viewing an External Text File / 9.9.1:
Selecting Variables - Var, Variables = / 10:
Selecting Variables in SAS and SPSS / 10.1:
Selecting All Variables / 10.2:
Selecting Variables by Index Number / 10.3:
Selecting Variables by Column Name / 10.4:
Selecting Variables Using Logic / 10.5:
Selecting Variables by String Search (varname: or varname1-varnameN) / 10.6:
Selecting Variables Using $ Notation / 10.7:
Selecting Variables by Simple Name: attach and with / 10.8:
Selecting Variables with the subset Function (varname1-varnameN) / 10.9:
Selecting Variables by List / 10.10:
Generating Indexes A to Z from Two Variable Names / 10.11:
Saving Selected Variables to a New Dataset / 10.12:
Example Programs for Variable Selection / 10.13:
Selecting Observations - Where, If, Select If, Filter / 11:
Selecting Observations in SAS and SPSS / 11.1:
Selecting All Observations / 11.2:
Selecting Observations by Index Number / 11.3:
Selecting Observations by Row Name / 11.4:
Selecting Observations Using Logic / 11.5:
Selecting Observations by String Search / 11.6:
Selecting Observations with the subset Function / 11.7:
Generating Indexes from A to Z from Two Row Names / 11.8:
Variable Selection Methods with No Counterpart for Selecting Observations / 11.9:
Saving Selected Observations to a New Data Frame / 11.10:
Example Programs for Selecting Observations / 11.11:
Selecting Both Variables and Observations / 12:
Converting Data Structures / 13:
Converting from Logical to Index and Back / 13.1:
Data Management / 14:
Transforming Variables / 14.1:
Procedures or Functions? The apply Function Decides / 14.2:
Applying the mean Function / 14.2.1:
Finding N or NVALID / 14.2.2:
Conditional Transformations / 14.3:
Multiple Conditional Transformations / 14.4:
Missing Values / 14.5:
Substituting Means for Missing Values / 14.5.1:
Finding Complete Observations / 14.5.2:
When "99" Has Meaning / 14.5.3:
Renaming Variables (... and Observations) / 14.6:
Renaming Variables - Advanced Examples / 14.7:
Renaming by Index / 14.7.1:
Renaming by Column Name / 14.7.2:
Renaming Many Sequentially Numbered Variable Names / 14.7.3:
Renaming Observations / 14.7.4:
Recoding Variables / 14.8:
Recoding a Few Variables / 14.8.1:
Recoding Many Variables / 14.8.2:
Keeping and Dropping Variables / 14.9:
Stacking/Concatenating/Adding Datasets / 14.10:
Joining/Merging Data Frames / 14.11:
Creating Summarized or Aggregated Datasets / 14.12:
The aggregate Function / 14.12.1:
The tapply Function / 14.12.2:
Merging Aggregates with Original Data / 14.12.3:
Tabular Aggregation / 14.12.4:
The reshape Package / 14.12.5:
By or Split File Processing / 14.13:
Comparing Summarization Methods / 14.13.1:
Example Programs for By or Split File Processing / 14.13.2:
Removing Duplicate Observations / 14.14:
Selecting First or Last Observations per Group / 14.15:
Reshaping Variables to Observations and Back / 14.16:
Sorting Data Frames / 14.17:
Value Labels or Formats (and Measurement Level) / 15:
Character Factors / 15.1:
Numeric Factors / 15.2:
Making Factors of Many Variables / 15.3:
Converting Factors into Numeric or Character Variables / 15.4:
Dropping Factor Levels / 15.5:
Variable Labels / 16:
Generating Data / 17:
Generating Numeric Sequences / 17.1:
Generating Factors / 17.2:
Generating Repetitious Patterns (not factors) / 17.3:
Generating Integer Measures / 17.4:
Generating Continuous Measures / 17.5:
Generating a Data Frame / 17.6:
How R Stores Data / 18:
Managing Your Files and Workspace / 19:
Loading and Listing Objects / 19.1:
Understanding Your Search Path / 19.2:
Attaching Data Frames / 19.3:
Attaching Files / 19.4:
Removing Objects from Your Workspace / 19.5:
Minimizing Your Workspace / 19.6:
Setting Your Working Directory / 19.7:
Saving Your Workspace / 19.8:
Saving Your Programs and Output / 19.9:
Saving Your History (Journal) / 19.10:
Graphics Overview / 20:
SAS/GRAPH / 20.1:
SPSS Graphics / 20.2:
R Graphics / 20.3:
The Grammar of Graphics / 20.4:
Other Graphics Packages / 20.5:
Graphics Procedures Versus Graphics Systems / 20.6:
Graphics Devices / 20.7:
Practice Data: Mydata100 / 20.8:
Traditional Graphics / 21:
Barplots / 21.1:
Barplots of Counts / 21.1.1:
Barplots for Subgroups of Counts / 21.1.2:
Barplots of Means / 21.1.3:
Adding Titles, Labels, Colors, and Legends / 21.2:
Graphics Parameters and Multiple Plots on a Page / 21.3:
Pie Charts / 21.4:
Dotcharts / 21.5:
Histograms / 21.6:
Basic Histograms / 21.6.1:
Histograms Overlaid / 21.6.2:
Normal QQ Plots / 21.7:
Strip Charts / 21.8:
Scatterplots / 21.9:
Scatterplots with Jitter / 21.9.1:
Scatterplots with Large Datasets / 21.9.2:
Scatterplots with Lines / 21.9.3:
Scatterplots with Linear Fit by Group / 21.9.4:
Scatterplots by Group or Level (Coplots) / 21.9.5:
Scatterplots with Confidence Ellipse / 21.9.6:
Scatterplots with Confidence and Prediction Intervals / 21.9.7:
Plotting Labels Instead of Points / 21.9.8:
Scatterplot Matrices / 21.9.9:
Dual Axes Plots / 21.10:
Boxplots / 21.11:
Error Bar and Interaction Plots / 21.12:
Adding Equations and Symbols to Graphs / 21.13:
Summary of Graphics Elements and Parameters / 21.14:
Plot Demonstrating Many Modifications / 21.15:
Example Traditional Graphics Programs / 21.16:
Graphics with ggplot2 (GPL) / 22:
Overview qplot and ggplot / 22.1:
Bar Charts / 22.2:
Bar Charts with Subgroups / 22.3:
Plots by Group or Level / 22.5:
Pre-summarized Data / 22.6:
Adding Titles and Labels / 22.7:
Strip Plots / 22.9:
Scatterplots with Fit Lines / 22.12:
Scatterplots with Reference Lines / 22.15:
Changing Plot Symbols by Group / 22.16:
Adding Linear Fits by Group / 22.18:
Scatterplots Faceted by Groups / 22.19:
Scatterplot Matrix / 22.20:
Error Barplots / 22.21:
Logarithmic Axes / 22.23:
Aspect Ratio / 22.24:
Multiple Plots on a Page / 22.25:
Saving ggplot2 Graphs to a File / 22.26:
An Example Specifying All Defaults / 22.27:
Summary of Graphic Elements and Parameters / 22.28:
Statistics / 23:
Scientific Notation / 23.1:
Descriptive Statistics / 23.2:
Cross-Tabulation / 23.3:
Correlation / 23.4:
Linear Regression / 23.5:
Plotting Diagnostics / 23.5.1:
Comparing Models / 23.5.2:
Making Predictions with New Data / 23.5.3:
t-Test - Independent Groups / 23.6:
Equality of Variance / 23.7:
t-Test - Paired or Repeated Measures / 23.8:
Wilcoxon Mann-Whitney Rank Sum Test - Independent Groups / 23.9:
Wilcoxon Signed-Rank Test - Paired Groups / 23.10:
Analysis of Variance / 23.11:
Sums of Squares / 23.12:
Kruskal-Wallis Test / 23.13:
Conclusion / 24:
A Glossary of R Jargon / Appendix A:
A Comparison of SAS and SPSS Products with R Packages and Functions / Appendix B:
Automating Your Settings / Appendix C:
A comparison of the major attributes of SAS and SPSS to R / Appendix D:
Bibliography
Index
Introduction / 1:
Why Learn R? / 1.1:
Is R Accurate? / 1.2:
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