3rd Edition

Biosignal and Medical Image Processing

By John L. Semmlow, Benjamin Griffel Copyright 2014
    630 Pages 333 B/W Illustrations
    by CRC Press

    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

    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