Biosignal and medical image processing

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Biosignal and medical image processing / John L. Semmlow

版:: 2nd ed
資料種別:: 図書
出版情報:: Boca Raton : CRC Press, c2009
形態:: xvii, 450 p. ; 27 cm. + 1 CD-ROM (12 cm.)
著者名:: Semmlow, John L. <DA14405951>
ISBN:: 9781420062304 [1420062301] (: hbk)
書誌ID:: BA88170064

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注記:

Includes bibliographical references (p. 433-435) and index

主題:

Diagnostic imaging--Digital techniques; Image processing; Imaging systems in medicine

言語:

英語

目次情報:

Preface

Acknowledgments

Author

Introduction / Chapter 1：

Typical Measurement Systems / 1.1：

Transducers / 1.1.1：

Further Study: The Transducer / 1.1.2：

Analog Signal Processing / 1.1.3：

Sources of Variability: Noise / 1.2：

Electronic Noise / 1.2.1：

Signal-to-Noise Ratio / 1.2.2：

Analog Filters: Filter Basics / 1.3：

Filter Types / 1.3.1：

Filter Bandwidth / 1.3.2：

Filter Order / 1.3.3：

Filter Initial Sharpness / 1.3.4：

Analog-to-Digital Conversion: Basic Concepts / 1.4：

Analog-to-Digital Conversion Techniques / 1.4.1：

Quantization Error / 1.4.2：

Further Study: Successive Approximation Analog-to-Digital Conversion / 1.4.3：

Time Sampling: Basics / 1.5：

Further Study: Buffering and Real-Time Data Processing / 1.5.1：

Data Banks / 1.6：

Problems

Basic Concepts / Chapter 2：

Noise / 2.1：

Ensemble Averaging / 2.1.1：

MATLAB Implementation / 2.1.2：

Data Functions and Transforms / 2.2：

Comparing Waveforms: Vector Representation / 2.2.1：

Signal Analysis: Transformation and Basis Functions / 2.2.2：

Convolution, Correlation, and Covariance / 2.3：

Convolution and the Impulse Response / 2.3.1：

Covariance and Correlation / 2.3.2：

Covariance, Correlation, and Autocorrelation Matrices / 2.3.3：

Sampling Theory and Finite Data Considerations / 2.3.4：

Edge Effects / 2.4.1：

Spectral Analysis: Classical Methods / Chapter 3：

Fourier Transform: Fourier Series Analysis / 3.1：

Periodic Functions / 3.2.1：

Symmetry / 3.2.1.1：

Discrete-Time Fourier Analysis / 3.2.2：

Aperiodic Functions / 3.3：

Frequency Resolution / 3.3.1：

MATLAB Implementation: Direct FFT / 3.4：

Truncated Fourier Analysis: Data Windowing / 3.5：

MATLAB Implementation: Window Functions / 3.6：

Power Spectrum / 3.7：

MATLAB Implementation: The Welch Method for Power Spectral Density Determination / 3.8：

Digital Filters / Chapter 4：

Z-Transform / 4.1：

Digital Transfer Function / 4.2.1：

Finite Impulse Response (FIR) Filters / 4.2.2：

FIR Filter Design / 4.3.1：

Derivative Operation: The Two-Point Central Difference Algorithm / 4.3.2：

Filter Design and Application Using the MATLAB Signal Processing Toolbox / 4.3.3：

Single-Stage FIR Filter Design / 4.3.4.1：

Two-Stage FIR Filter Design / 4.3.4.2：

Infinite Impulse Response (IIR) Filters / 4.4：

MATLAB Implementation IIR Filters / 4.4.1：

Single-Stage IIR Filter Design / 4.4.2：

Two-Stage IIR Filter Design: Analog Style Filters / 4.4.3：

Spectral Analysis: Modern Techniques / Chapter 5：

Parametric Methods / 5.1：

Yule-Walker Equations / 5.1.1：

Nonparametric Analysis: Eigenanalysis Frequency Estimation / 5.1.2：

Time-Frequency Analysis / 5.2.1：

Basic Approaches / 6.1：

Short-Term Fourier Transform: The Spectrogram / 6.2：

MATLAB Implementation: The Short-Term Fourier Transform / 6.2.1：

Wigner-Ville Distribution: A Special Case of Cohen's Class / 6.3：

Instantaneous Autocorrelation Function / 6.3.1：

Choi-Williams and Other Distributions / 6.4：

Analytic Signal / 6.4.1：

Wigner-Ville Distribution / 6.5：

Wavelet Analysis / 6.5.2：

Continuous Wavelet Transform / 7.1：

Wavelet Time-Frequency Characteristics / 7.2.1：

Discrete Wavelet Transform / 7.2.2：

Filter Banks / 7.3.1：

Relationship between Analytical Expressions and Filter Banks / 7.3.1.1：

Denoising / 7.3.2：

Discontinuity Detection / 7.3.2.2：

Feature Detection: Wavelet Packets / 7.4：

Advanced Signal Processing Techniques: Optimal and Adaptive Filters / Chapter 8：

Optimal Signal Processing: Wiener Filters / 8.1：

Adaptive Signal Processing / 8.1.1：

Adaptive Line Enhancement (ALE) and Adaptive Interference Suppression / 8.2.1：

Adaptive Noise Cancellation (ANC) / 8.2.2：

Phase-Sensitive Detection / 8.2.3：

AM Modulation / 8.3.1：

Phase-Sensitive Detectors / 8.3.2：

Multivariate Analyses: Principal Component Analysis and Independent Component Analysis / 8.3.3：

Introduction: Linear Transformations / 9.1：

Principal Component Analysis / 9.2：

Determination of Principal Components Using Singular Value Decomposition / 9.2.1：

Order Selection: The Scree Plot / 9.2.2：

Data Rotation / 9.2.3：

PCA Evaluation / 9.2.4：

Independent Component Analysis / 9.3：

Fundamentals of Image Processing: MATLAB Image Processing Toolbox / 9.3.1：

Image Processing Basics: MATLAB Image Formats / 10.1：

General Image Formats: Image Array Indexing / 10.1.1：

Data Classes: Intensity Coding Schemes / 10.1.2：

Data Formats / 10.1.3：

Data Conversions / 10.1.4：

Image Display / 10.2：

Image Storage and Retrieval / 10.3：

Basic Arithmetic Operations / 10.4：

Advanced Protocols: Block Processing / 10.5：

Sliding Neighborhood Operations / 10.5.1：

Distinct Block Operations / 10.5.2：

Spectral Analysis: The Fourier Transform / Chapter 11：

Two-Dimensional Fourier Transform / 11.1：

Linear Filtering / 11.1.1：

Filter Design / 11.2.1：

Spatial Transformations / 11.3：

Affine Transformations / 11.3.1：

General Affine Transformations / 11.3.1.2：

Projective Transformations / 11.3.1.3：

Image Registration / 11.4：

Unaided Image Registration / 11.4.1：

Interactive Image Registration / 11.4.2：

Image Segmentation / Chapter 12：

Pixel-Based Methods / 12.1：

Threshold Level Adjustment / 12.2.1：

Continuity-Based Methods / 12.2.2：

Multithresholding / 12.3.1：

Morphological Operations / 12.5：

Edge-Based Segmentation / 12.5.1：

Hough Transform / 12.6.1：

Image Reconstruction / 12.6.2：

CT, PET, SPECT / 13.1：

Filtered Back-Projection / 13.1.2：

Fan Beam Geometry / 13.1.3：

Radon Transform / 13.1.4：

Inverse Radon Transform: Parallel Beam Geometry / 13.1.4.2：

Radon and Inverse Radon Transform: Fan Beam Geometry / 13.1.4.3：

Magnetic Resonance Imaging / 13.2：

Basic Principles / 13.2.1：

Data Acquisition: Pulse Sequences / 13.2.2：

Functional MRI / 13.3：

Principal Component and Independent Component Analyses / 13.3.1：

Classification I: Linear Discriminant Analysis and Support Vector Machines / Chapter 14：

Classifier Design / 14.1：

Linear Discriminators / 14.2：

Evaluating Classifier Performance / 14.3：

Higher Dimensions: Kernel Machines / 14.4：

Support Vector Machines / 14.5：

Machine Capacity: Overfitting or "Less Is More" / 14.5.1：

Cluster Analysis / 14.7：

The k-Nearest Neighbor Classifier / 14.7.1：

The k-Means Clustering Classifier / 14.7.2：

Adaptive Neural Nets / Chapter 15：

Neuron Models / 15.1：

McCullough-Pitts Neural Nets / 15.2：

Gradient Descent Method or Delta Rule / 15.3：

Two-Layer Nets: Backpropagation / 15.4：

Three-Layer Nets / 15.5：

Training Strategies / 15.6：

Stopping Criteria: Cross-Validation / 15.6.1：

Momentum / 15.6.2：

Multiple Classifications / 15.7：

Multiple Input Variables / 15.8：

Annotated Bibliography

Index

Preface

Acknowledgments

Author

Introduction / Chapter 1：

Typical Measurement Systems / 1.1：

Transducers / 1.1.1：

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