Biosignal and Medical Image Processing: 3rd Edition (Hardback) book cover

Biosignal and Medical Image Processing

3rd Edition

By John L. Semmlow, Benjamin Griffel

CRC Press

630 pages | 333 B/W Illus.

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Hardback: 9781466567368
pub: 2014-02-25
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Description

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.

Reviews

"…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

Table of Contents

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

Subject Categories

BISAC Subject Codes/Headings:
MED009000
MEDICAL / Biotechnology
TEC015000
TECHNOLOGY & ENGINEERING / Imaging Systems
TEC059000
TECHNOLOGY & ENGINEERING / Biomedical