Introduction
xxiii
Knowledge of linear algebra would also be helpful in understanding the concepts.
The book describes the mathematical concepts in signal and image processing techniques in great detail and, as a result, no prior knowledge of fundamental processing
techniques (such as Fourier transform) is required. At the same time, for readers
who are already familiar with the main signal processing concepts, the chapters
dedicated to signal and image processing techniques can serve as a detailed review
of this field.
Part I provides a detailed description of the main signal processing, image processing, and pattern recognition techniques. The chapters in this part also cover the
main computational methods in other fields of study such as information theory and
stochastic processes. The combination of all these mathematical techniques provides
the computational skills needed to analyze biomedical signal and images. Readers
who have previously taken courses in all related areas, such as digital signal, image
processing, information theory, and pattern recognition, are also recommended to
read through Part II to familiarize themselves with the notation and practice applying their computational skills to biomedical data.
Even though the authors emphasize the importance of mathematical concepts covered in the book, they strongly believe that the best method of learning the math
concepts is through doing real examples. As a result, each chapter contains several
programming examples written in MATLAB ® that process real biomedical signals/
images using the respective mathematical methods. These examples are designed
to help the reader better understand the math concepts. Even though the book is not
intended to teach MATLAB, the increasing level of difficulty in the MATLAB examples allows the reader to gradually improve his or her MATLAB programming skills.
Each chapter also contains a number of exercises in the Problems section that
give students the chance to practice the introduced techniques. Some of the problems are designed to help students improve their knowledge of the mathematical
concepts, while the rest are practical problems defined using real data from biomedical systems (appearing on the companion website to the book). Specifically, while
some of the problems are mainly mathematical problems to be done manually, the
vast majority of the problems in all chapters are programming problems designed
to help the readers obtain hands-on experience in dealing with real-world problems.
Virtually all these problems apply the methods introduced in the previous chapters
to real problems in biomedical signal and image processing applications.
Part II introduces the major one-dimensional biomedical signals. In each chapter,
at first the biological origin and importance of the signal are explained, followed by
a description of the main computational methods commonly used for processing the
signal. Assuming that readers have acquired the signal/image processing skills in
Part I, the main focus of Part II is on the physiology and diagnostic applications of
the biomedical signals. Almost all examples and exercises in these chapters use real
biomedical data for real biomedical signal processing applications.
The last part, Part III, deals with the main biomedical image modalities. It first
covers the physical and philological principles of imaging modalities and subsequently describes the main applications of the introduced imaging modalities in
biomedical diagnostics. In each chapter, the main computational methods used to
process these images are also reviewed.
xxiii
Knowledge of linear algebra would also be helpful in understanding the concepts.
The book describes the mathematical concepts in signal and image processing techniques in great detail and, as a result, no prior knowledge of fundamental processing
techniques (such as Fourier transform) is required. At the same time, for readers
who are already familiar with the main signal processing concepts, the chapters
dedicated to signal and image processing techniques can serve as a detailed review
of this field.
Part I provides a detailed description of the main signal processing, image processing, and pattern recognition techniques. The chapters in this part also cover the
main computational methods in other fields of study such as information theory and
stochastic processes. The combination of all these mathematical techniques provides
the computational skills needed to analyze biomedical signal and images. Readers
who have previously taken courses in all related areas, such as digital signal, image
processing, information theory, and pattern recognition, are also recommended to
read through Part II to familiarize themselves with the notation and practice applying their computational skills to biomedical data.
Even though the authors emphasize the importance of mathematical concepts covered in the book, they strongly believe that the best method of learning the math
concepts is through doing real examples. As a result, each chapter contains several
programming examples written in MATLAB ® that process real biomedical signals/
images using the respective mathematical methods. These examples are designed
to help the reader better understand the math concepts. Even though the book is not
intended to teach MATLAB, the increasing level of difficulty in the MATLAB examples allows the reader to gradually improve his or her MATLAB programming skills.
Each chapter also contains a number of exercises in the Problems section that
give students the chance to practice the introduced techniques. Some of the problems are designed to help students improve their knowledge of the mathematical
concepts, while the rest are practical problems defined using real data from biomedical systems (appearing on the companion website to the book). Specifically, while
some of the problems are mainly mathematical problems to be done manually, the
vast majority of the problems in all chapters are programming problems designed
to help the readers obtain hands-on experience in dealing with real-world problems.
Virtually all these problems apply the methods introduced in the previous chapters
to real problems in biomedical signal and image processing applications.
Part II introduces the major one-dimensional biomedical signals. In each chapter,
at first the biological origin and importance of the signal are explained, followed by
a description of the main computational methods commonly used for processing the
signal. Assuming that readers have acquired the signal/image processing skills in
Part I, the main focus of Part II is on the physiology and diagnostic applications of
the biomedical signals. Almost all examples and exercises in these chapters use real
biomedical data for real biomedical signal processing applications.
The last part, Part III, deals with the main biomedical image modalities. It first
covers the physical and philological principles of imaging modalities and subsequently describes the main applications of the introduced imaging modalities in
biomedical diagnostics. In each chapter, the main computational methods used to
process these images are also reviewed.