Region
Biosignal and Medical Image Processing, Second Edition
2nd Edition
By John L. Semmlow
- Price: $99.95
- Binding/Format: Hardback
- ISBN: 978-1-4200623-0-4
- Publish Date: October 24th 2008
- Imprint: CRC Press
- Pages: 472 pages
Series: Signal Processing and Communications
Description
A Practical Guide to Signal Processing Methodology
Just as a cardiologist can benefit from an oscilloscope-type display of the ECG without a deep understanding of electronics, an engineer can benefit from advanced signal processing tools without always understanding the details of the underlying mathematics. Through the use of extensive MATLAB® examples and problems, Biosignal and Medical Image Processing, Second Edition provides readers with the necessary knowledge to successfully evaluate and apply a wide range of signal and image processing tools.
The book begins with an extensive introductory section and a review of basic concepts before delving into more complex areas. Topics discussed include classical spectral analysis, basic digital filtering, advanced spectral methods, spectral analysis for time-variant spectrums, continuous and discrete wavelets, optimal and adaptive filters, and principal and independent component analysis. In addition, image processing is discussed in several chapters with examples taken from medical imaging. Finally, new to this second edition are two chapters on classification that review linear discriminators, support vector machines, cluster techniques, and adaptive neural nets.
Comprehensive yet easy to understand, this revised edition of a popular volume seamlessly blends theory with practical application. Most of the concepts are presented first by providing a general understanding, and second by describing how the tools can be implemented using the MATLAB software package.
Through the concise explanations presented in this volume, readers gain an understanding of signal and image processing that enables them to apply advanced techniques to applications without the need for a complex understanding of the underlying mathematics.
A solutions manual is available for instructors wishing to convert this reference to classroom use.
Contents
Introduction
Typical Measurement Systems
Sources of Variability: Noise
Analog Filters: Filter Basics
Analog-to-Digital Conversion: Basic Concepts
Time Sampling: Basics
Data Banks
Problems
Basic Concepts
Noise
Data Functions and Transforms
Convolution, Correlation, and Covariance
Sampling Theory and Finite Data Considerations
Problems
Spectral Analysis: Classical Methods
Introduction
The Fourier Transform: Fourier Series Analysis
Aperiodic Functions
MATLAB Implementation: Direct FFT
Truncated Fourier Analysis: Data Windowing
MATLAB Implementation: Window Functions
Power Spectrum
MATLAB Implementation: The Welch Method for
Power Spectral Density Determination
Problems
Digital Filters
Introduction
The Z-Transform
Finite Impulse Response (FIR) Filters
Infinite Impulse Response (IIR) Filters
Problems
Spectral Analysis: Modern Techniques
Parametric Methods
Nonparametric Analysis: Eigenanalysis Frequency Estimation
Problems
Time–Frequency Analysis
Basic Approaches
Short-Term Fourier Transform: The Spectrogram
The Wigner-Ville Distribution: A Special Case of Cohen’s Class
The Choi-Williams and Other Distributions
MATLAB Implementation
Problems
Wavelet Analysis
Introduction
The Continuous Wavelet Transform
The Discrete Wavelet Transform
Feature Detection: Wavelet Packets
Problems
Advanced Signal Processing Techniques: Optimal and Adaptive Filters
Optimal Signal Processing: Wiener Filters
Adaptive Signal Processing
Phase-Sensitive Detection
Problems
Multivariate Analyses: Principal Component Analysis and Independent Component Analysis
Introduction: Linear Transformations
Principal Component Analysis
Independent Component Analysis
Problems
Fundamentals of Imaging Processing: MATLAB Image Processing Toolbox
Image Processing Basics: MATLAB Image Formats
Image Display
Image Storage and Retrieval
Basic Arithmetic Operations
Advanced Protocols: Block Processing
Problems
Spectral Analysis: The Fourier Transform
The Two-Dimensional Fourier Transform
Linear Filtering
Spatial Transformations
Image Registration
Problems
Image Segmentation
Introduction
Pixel-Based Methods
Continuity-Based Methods
Multithresholding
Morphological Operations
Edge-Based Segmentation
Problems
Image Reconstruction
Introduction
Magnetic Resonance Imaging
Functional MRI
Problems
Classification I: Linear Discriminant Analysis and Support Vector Machines
Introduction
Linear Discriminators
Evaluating Classifier Performance
Higher Dimensions: Kernel Machines
Support Vector Machines
Machine Capacity: Overfitting or "Less Is More"
Cluster Analysis
Problems
Adaptive Neural Nets
Introduction
McCullough-Pitts Neural Nets
The Gradient Descent Method or Delta Rule
Two-Layer Nets: Back-Projection
Three-Layer Nets
Training Strategies
Multiple Classifications
Multiple Input Variables
Problems
Annotated Bibliography
: AM