Biosignal and Biomedical Image Processing MATLA B-Based Applications JOHN L. SEMMLOW Robert Wood Johnson Medical School New Brunswick, New Jersey, U.S.A. Rutgers University Piscataway, New Jersey, U.S.A. Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved. Although great care has been taken to provide accurate and current information, neither the author(s) nor the publisher, nor anyone else associated with this publication, shall be liable for any loss, damage, or liability directly or indirectly caused or alleged to be caused by this book. The material contained herein is not intended to provide specific advice or recommendations for any specific situation. Trademark notice: Product or corporate names may be trademarks or registered trade- marks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress. ISBN: 0–8247-4803–4 This book is printed on acid-free paper. Headquarters Marcel Dekker, Inc., 270 Madison Avenue, New York, NY 10016, U.S.A. tel: 212-696-9000; fax: 212-685-4540 Distribution and Customer Service Marcel Dekker, Inc., Cimarron Road, Monticello, New York 12701, U.S.A. tel: 800-228-1160; fax: 845-796-1772 Eastern Hemisphere Distribution Marcel Dekker AG, Hutgasse 4, Postfach 812, CH-4001 Basel, Switzerland tel: 41-61-260-6300; fax: 41-61-260-6333 World Wide Web http://www.dekker.com The publisher offers discounts on this book when ordered in bulk quantities. For more information, write to Special Sales/Professional Marketing at the headquarters address above. Copyright  2004 by Marcel Dekker, Inc. All Rights Reserved. Neither this book nor any part may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, microfilming, and recording, or by any information storage and retrieval system, without permission in writing from the publisher. Current printing (last digit): 10987654321 PRINTED IN THE UNITED STATES OF AMERICA Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved. Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved. Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved. To Lawrence Stark, M.D., who has shown me the many possibilities Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved. Series Introduction Over the past 50 years, digital signal processing has evolved as a major engi- neering discipline. The fields of signal processing have grown from the origin of fast Fourier transform and digital filter design to statistical spectral analysis and array processing, image, audio, and multimedia processing, and shaped de- velopments in high-performance VLSI signal processor design. Indeed, there are few fields that enjoy so many applications—signal processing is everywhere in our lives. When one uses a cellular phone, the voice is compressed, coded, and modulated using signal processing techniques. As a cruise missile winds along hillsides searching for the target, the signal processor is busy processing the images taken along the way. When we are watching a movie in HDTV, millions of audio and video data are being sent to our homes and received with unbeliev- able fidelity. When scientists compare DNA samples, fast pattern recognition techniques are being used. On and on, one can see the impact of signal process- ing in almost every engineering and scientific discipline. Because of the immense importance of signal processing and the fast- growing demands of business and industry, this series on signal processing serves to report up-to-date developments and advances in the field. The topics of interest include but are not limited to the following: • Signal theory and analysis • Statistical signal processing • Speech and audio processing Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved. • Image and video processing • Multimedia signal processing and technology • Signal processing for communications • Signal processing architectures and VLSI design We hope this series will provide the interested audience with high-quality, state-of-the -art signal processing literature through research monographs, edited books, and rigorously written textbooks by experts in their fields. Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved. Preface Signal processing can be broadly defined as the application of analog or digital techniques to improve the utility of a data stream. In biomedical engineering applications, improved utility usually means the data provide better diagnostic information. Analog techniques are applied to a data stream embodied as a time- varying electrical signal while in the digital domain the data are represented as an array of numbers. This array could be the digital representation of a time - varying signal, or an image. This text deals exclusively with signal processing of digital data, although Chapter 1 briefly describes analog processes commonly found in medical devices. This text should be of interest to a broad spectrum of engineers, but it is written specifically for biomedical engineers (also known as bioengineers). Although the applications are different, the signal processing methodology used by biomedical engineers is identical to that used by other engineers such electri- cal and communications engineers. The major difference for biomedical engi- neers is in the level of understanding required for appropriate use of this technol- ogy. An electrical engineer may be required to expand or modify signal processing tools, while for biomedical engineers, signal processing techniques are tools to be used. For the biomedical engineer, a detailed understanding of the underlying theory, while always of value, may not be essential. Moreover, considering the broad range of knowledge required to be effective in this field, encompassing both medical and engineering domains, an in-depth understanding of all of the useful technology is not realistic. It is important is to know what Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved. tools are available, have a good understanding of what they do (if not how they do it), be aware of the most likely pitfalls and misapplications, and know how to implement these tools given available software packages. The basic concept of this text is that, just as the cardiologist can benefit from an oscilloscope-type display of the ECG without a deep understanding of electronics, so a biomedical engineer can benefit from advanced signal processing tools without always un- derstanding the details of the underlying mathematics. As a reflection of this philosophy, most of the concepts covered in this text are presented in two sections. The first part provides a broad, general under- standing of the approach sufficient to allow intelligent application of the con- cepts. The second part describes how these tools can be implemented and relies primarily on the MATLAB software package and several of its toolboxes. This text is written for a single-semester course combining signal and image processing. Classroom experience using notes from this text indicates that this ambitious objective is possible for most graduate formats, although eliminating a few topics may be desirable. For example, some of the introduc- tory or basic material covered in Chapters 1 and 2 could be skipped or treated lightly for students with the appropriate prerequisites. In addition, topics such as advanced spectral methods (Chapter 5), time-frequency analysis (Chapter 6), wavelets (Chapter 7), advanced filters (Chapter 8), and multivariate analysis (Chapter 9) are pedagogically independent and can be covered as desired with- out affecting the other material. Although much of the material covered here will be new to most students, the book is not intended as an “introductory” text since the goal is to provide a working knowledge of the topics presented without the need for additional course work. The challenge of covering a broad range of topics at a useful, working depth is motivated by current trends in biomedical engineering educa- tion, 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 in the Graduate Program of Biomedical Engineering at Rutgers University. It is also quite suitable for an upper-level undergraduate course and would be of value for students in other disciplines who would benefit from a working knowledge of signal and image processing. It would not be possible to cover such a broad spectrum of material to a depth that enables productive application without heavy reliance on MATLAB- based examples and problems. In this regard, the text assumes the student has some knowledge of MATLAB programming and has available the basic MATLAB software package including the Signal Processing and Image Process- ing Toolboxes. (MATLAB also produces a Wavelet Toolbox, but the section on wavelets is written so as not to require this toolbox, primarily to keep the num- ber of required toolboxes to a minimum. ) The problems are an essential part of Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved. this text and often provide a discovery-like experience regarding the associated topic. A few peripheral topics are introduced only though the problems. The code used for all examples is provided in the CD accompanying this text. Since many of the problems are extensions or modifications of examples given in the chapter, some of the coding time can be reduced by starting with the code of a related example. The CD also includes support routines and data files used in the examples and problems. Finally, the CD contains the code used to generate many of the figures. For instructors, there is a CD available that contains the problem solutions and Powerpoint presentations from each of the chapters. These presentations include figures, equations, and text slides related to chapter. Presentations can be modified by the instructor as desired. In addition to heavy reliance on MATLAB problems and examples, this text makes extensive use of simulated data. Except for the section on image processing, examples involving biological signals are rarely used. In my view, examples using biological signals provide motivation, but they are not generally very instructive. Given the wide range of material to be presented at a working depth, emphasis is placed on learning the tools of signal processing; motivation is left to the reader (or the instructor). Organization of the text is straightforward. Chapters 1 through 4 are fairly basic. Chapter 1 covers topics related to analog signal processing and data acqui- sition while Chapter 2 includes topics that are basic to all aspects of signal and image processing. Chapters 3 and 4 cover classical spectral analysis and basic digital filtering, topics fundamental to any signal processing course. Advanced spectral methods, covered in Chapter 5, are important due to their widespread use in biomedical engineering. Chapter 6 and the first part of Chapter 7 cover topics related to spectral analysis when the signal’s spectrum is varying in time, a condition often found in biological signals. Chapter 7 also covers both contin- uous and discrete wavelets, another popular technique used in the analysis of biomedical signals. Chapters 8 and 9 feature advanced topics. In Chapter 8, optimal and adaptive filters are covered, the latter’s inclusion is also motivated by the time-varying nature of many biological signals. Chapter 9 introduces multivariate techniques, specifically principal component analysis and indepen- dent component analysis, two analysis approaches that are experiencing rapid growth with regard to biomedical applications. The last four chapters cover image processing, with the first of these, Chapter 10, covering the conventions used by MATLAB’s Imaging Processing Toolbox. Image processing is a vast area and the material covered here is limited primarily to areas associated with medical imaging: image acquisition (Chapter 13); image filtering, enhancement, and trans forma tion (Chapt er 11); and segmen tatio n, and registrati on (Chapter 12). Many of the chapters cover topics that can be adequately covered only in a book dedicated solely to these topics. In this sense, every chapter represents a serious compromise with respect to comprehensive coverage of the associated Copyright 2004 by Marcel Dekker, Inc. All Rights Reserved. [...]... Transformations Projective Transformations Image Registration Unaided Image Registration Interactive Image Registration Problems 12 Image Segmentation Pixel -Based Methods Threshold Level Adjustment MATLAB Implementation Continuity -Based Methods MATLAB Implementation Multi-Thresholding Morphological Operations MATLAB Implementation Edge -Based Segmentation MATLAB Implementation Problems Copyright 2004... Component Analysis and Independent Component Analysis Introduction Principal Component Analysis Order Selection MATLAB Implementation Data Rotation Principal Component Analysis Evaluation Independent Component Analysis MATLAB Implementation Problems 10 Fundamentals of Image Processing: MATLAB Image Processing Toolbox Image Processing Basics: MATLAB Image Formats General Image Formats: Image Array Indexing... Excellent coverage of Fourier analysis, and autoregressive methods Good introduction to statistical signal processing concepts Sonka, M., Hlavac V., and Boyle R Image processing, analysis, and machine vision Chapman and Hall Computing, London, 1993 A good description of edge -based and other segmentation methods Strang, G and Nguyen, T Wavelets and Filter Banks, Wellesley-Cambridge Press, Wellesley, MA, 1997... time-frequency distributions Bruce, E N Biomedical Signal Processing and Signal Modeling, John Wiley and Sons, Copyright 2004 by Marcel Dekker, Inc All Rights Reserved New York, 2001 Rigorous treatment with more of an emphasis on linear systems than signal processing Introduces nonlinear concepts such as chaos Cichicki, A and Amari S Adaptive Bilnd Signal and Image Processing: Learning Algorithms and. .. Filters Filter Design and Application Using the MATLAB Signal Processing Toolbox FIR Filters Two-Stage FIR Filter Design Three-Stage Filter Design IIR Filters Two-Stage IIR Filter Design Three-Stage IIR Filter Design: Analog Style Filters Problems 5 Spectral Analysis: Modern Techniques Parametric Model -Based Methods MATLAB Implementation Non-Parametric Eigenanalysis Frequency Estimation MATLAB Implementation... Short-Term Fourier Transform: The Spectrogram Wigner-Ville Distribution: A Special Case of Cohen’s Class Choi-Williams and Other Distributions Analytic Signal MATLAB Implementation The Short-Term Fourier Transform Wigner-Ville Distribution Choi-Williams and Other Distributions Problems 7 The Wavelet Transform Introduction The Continuous Wavelet Transform Wavelet Time—Frequency Characteristics MATLAB. .. real-time implementation of the Wigner-Ville Distribution, IEEE Trans Acoust Speech Sig Proc ASSP-35:1611–1618, 1987 Practical information on calculating the Wigner-Ville distribution Boudreaux-Bartels, G F and Murry, R Time-frequency signal representations for biomedical signals In: The Biomedical Engineering Handbook J Bronzino (ed.) CRC Press, Boca Raton, Florida and IEEE Press, Piscataway, N.J., 1995... Variability: Noise Electronic Noise Signal-to-Noise Ratio Analog Filters: Filter Basics Filter Types Filter Bandwidth Filter Order Filter Initial Sharpness Analog-to-Digital Conversion: Basic Concepts Analog-to-Digital Conversion Techniques Quantization Error Further Study: Successive Approximation Time Sampling: Basics Further Study: Buffering and Real-Time Data Processing Copyright 2004 by Marcel Dekker,... read by a non-signal processing friend Ingle, V.K and Proakis, J G Digital Signal Processing with MATLAB, Brooks/Cole, Inc Pacific Grove, CA, 2000 Excellent treatment of classical signal processing methods including the Fourier transform and both FIR and IIR digital filters Brief, but informative section on adaptive filtering Jackson, J E A User’s Guide to Principal Components, John Wiley and Sons, New... Conversions Image Display Image Storage and Retrieval Basic Arithmetic Operations Advanced Protocols: Block Processing Sliding Neighborhood Operations Distinct Block Operations Problems 11 Image Processing: Filters, Transformations, and Registration Spectral Analysis: The Fourier Transform MATLAB Implementation Linear Filtering MATLAB Implementation Filter Design Spatial Transformations MATLAB Implementation . some knowledge of MATLAB programming and has available the basic MATLAB software package including the Signal Processing and Image Process- ing Toolboxes. (MATLAB also produces a Wavelet Toolbox,. Biosignal and Biomedical Image Processing MATLA B -Based Applications JOHN L. SEMMLOW Robert Wood Johnson Medical School New Brunswick, New Jersey, U.S.A analog signal processing and data acqui- sition while Chapter 2 includes topics that are basic to all aspects of signal and image processing. Chapters 3 and 4 cover classical spectral analysis and