Biosignal and Medical Image Processing

Introduction
Biosignals
Biosignal Measurement Systems
Transducers
Amplifier/Detector
Analog Signal Processing and Filters
ADC Conversion
Data Banks
Summary
Problems

Biosignal Measurements, Noise, and Analysis
Biosignals
Noise
Signal Analysis: Data Functions and Transforms
Summary
Problems

Spectral Analysis: Classical Methods
Introduction
Fourier Series Analysis
Power Spectrum
Spectral Averaging: Welch’s Method
Summary
Problems

Noise Reduction and Digital Filters
Noise Reduction
Noise Reduction through Ensemble Averaging
Z-Transform
Finite Impulse Response Filters
Infinite Impulse Response Filters
Summary
Problems

Modern Spectral Analysis: The Search for Narrowband Signals
Parametric Methods
Nonparametric Analysis: Eigenanalysis Frequency Estimation
Problems

TimeFrequency Analysis
Basic Approaches
The Short-Term Fourier Transform: The Spectrogram
The WignerVille Distribution: A Special Case of Cohen’s Class
Cohen’s Class Distributions
Summary
Problems

Wavelet Analysis
Introduction
Continuous Wavelet Transform
Discrete Wavelet Transform
Feature Detection: Wavelet Packets
Summary
Problems

Optimal and Adaptive Filters
Optimal Signal Processing: Wiener Filters
8.2 Adaptive Signal Processing
8.3 Phase-Sensitive Detection
8.4 Summary
Problems

Multivariate Analyses: Principal Component Analysis and Independent Component Analysis
Introduction: Linear Transformations
Principal Component Analysis
Independent Component Analysis
Summary
Problems

Chaos and Nonlinear Dynamics
Nonlinear Systems
Phase Space
Estimating the Embedding Parameters
Quantifying Trajectories in Phase Space: The Lyapunov Exponent
Nonlinear Analysis: The Correlation Dimension
Tests for Nonlinearity: Surrogate Data Analysis
Summary
Exercises

Nonlinearity Detection: Information-Based Methods
Information and Regularity
Mutual Information Function
Spectral Entropy
Phase-Space-Based Entropy Methods
Detrended Fluctuation Analysis
Summary
Problems

Fundamentals of Image Processing: The MATLAB Image Processing Toolbox
Image-Processing Basics: MATLAB Image Formats
Image Display
Image Storage and Retrieval
Basic Arithmetic Operations
Block-Processing Operations
Summary
Problems

Image Processing: Filters, Transformations, and Registration
Two-Dimensional Fourier Transform
Linear Filtering
Spatial Transformations
Image Registration
Summary
Problems

Image Segmentation
Introduction
Pixel-Based Methods
Continuity-Based Methods
Multithresholding
Morphological Operations
Edge-Based Segmentation
Summary
Problems

Image Acquisition and Reconstruction
Imaging Modalities
CT, PET, and SPECT
Magnetic Resonance Imaging
Functional MRI
Summary
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"
Extending the Number of Variables and Classes
Cluster Analysis
Summary
Problems

Classification II: Adaptive Neural Nets
Introduction
Training the McCulloughPitts Neuron
The Gradient Decent Method or Delta Rule
Two-Layer Nets: Back Projection
Three-Layer Nets
Training Strategies
Multiple Classifications
Multiple Input Variables
Summary
Problems
Appendix A: Numerical Integration in MATLAB
Appendix B: Useful MATLAB Functions
Bibliography
Index

Biography

John L. Semmlow (Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA) (Author) , Benjamin Griffel (Author)

"…An excellent review of the actual trendiest techniques in signal processing with a very clear (and simplified) description of their capabilities in signal and image analysis. Matlab examples are an excellent addition to provide students with capabilities to understand better how the techniques work…"
–Enrique Nava Baro, PhD, University of MÁlaga, Spain

"The book is a welcome addition to the teaching literature for biomedical engineering, building on the previous edition’s friendly approach to introducing the material. This makes it particularly suitable for biomedical engineering, a field in which students come from a variety of backgrounds, and where familiarity of the fundamentals of electrical engineering cannot be assumed."
–David A. Clifton, University of Oxford, UK