Biosignal and Biomedical Image Processing [Matlab-Based Applns] - J. Semmlow (2004) WW

443 Pages·2004·7.54 MB·English

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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.

M A R C E L

MARCEL DEKKER,
INC.
'.p%
D E K K E R
NEW YORK BASEL

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Although great care has been taken to provide accurate and current information, neither
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ISBN: 0–8247-4803–4

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To Lawrence Stark, M.D., who has shown me the many possibilities . . .

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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

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•
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.

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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

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Preface

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

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ix

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 transformation (Chapter 11); and segmentation, and registration (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

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