3rd Edition
Biosignal and Medical Image Processing
Written specifically for biomedical engineers, Biosignal and Medical Image Processing, Third Edition provides a complete set of signal and image processing tools, including diagnostic decision-making tools, and classification methods. Thoroughly revised and updated, it supplies important new material on nonlinear methods for describing and classifying signals, including entropy-based methods and scaling methods. A full set of PowerPoint slides covering the material in each chapter and problem solutions is available to instructors for download.
See What’s New in the Third Edition:
- Two new chapters on nonlinear methods for describing and classifying signals.
- Additional examples with biological data such as EEG, ECG, respiration and heart rate variability
- Nearly double the number of end-of-chapter problems
- MATLAB® incorporated throughout the text
- Data "cleaning" methods commonly used in such areas as heart rate variability studies
The text provides a general understanding of image processing sufficient to allow intelligent application of the concepts, including a description of the underlying mathematical principals when needed. Throughout this textbook, signal and image processing concepts are implemented using the MATLAB® software package and several of its toolboxes.
The challenge of covering a broad range of topics at a useful, working depth is motivated by current trends in biomedical engineering education, particularly at the graduate level where a comprehensive education must be attained with a minimum number of courses. This has led to the development of "core" courses to be taken by all students. This text was written for just such a core course. It is also suitable for an upper-level undergraduate course and would also be of value for students in other disciplines that would benefit from a working knowledge of signal and 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
"…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
