Biosignal and Medical Image Processing: MATLAB-Based Applications / Edition 2

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A Practical Guide to Signal Processing Methodology

Just as a cardiologist can benefit from an oscilloscope-type display of the ECG without a deep understanding of electronics, an engineer can benefit from advanced signal processing tools without always understanding the details of the underlying mathematics. Through the use of extensive MATLAB® examples and problems, Biosignal and Medical Image Processing, Second Edition provides readers with the necessary knowledge to successfully evaluate and apply a wide range of signal and image processing tools.

The book begins with an extensive introductory section and a review of basic concepts before delving into more complex areas. Topics discussed include classical spectral analysis, basic digital filtering, advanced spectral methods, spectral analysis for time-variant spectrums, continuous and discrete wavelets, optimal and adaptive filters, and principal and independent component analysis. In addition, image processing is discussed in several chapters with examples taken from medical imaging. Finally, new to this second edition are two chapters on classification that review linear discriminators, support vector machines, cluster techniques, and adaptive neural nets.

Comprehensive yet easy to understand, this revised edition of a popular volume seamlessly blends theory with practical application. Most of the concepts are presented first by providing a general understanding, and second by describing how the tools can be implemented using the MATLAB software package.

Through the concise explanations presented in this volume, readers gain an understanding of signal and image processing that enables them to apply advanced techniques to applications without the need for a complex understanding of the underlying mathematics.

A solutions manual is available for instructors wishing to convert this reference to classroom use.

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Product Details

  • ISBN-13: 9781420062304
  • Publisher: CRC Press
  • Publication date: 10/24/2008
  • Series: Signal Processing and Communications Series
  • Edition description: REV
  • Edition number: 2
  • Pages: 472
  • Product dimensions: 7.10 (w) x 10.10 (h) x 1.00 (d)

Table of Contents


Typical Measurement Systems

Sources of Variability: Noise

Analog Filters: Filter Basics

Analog-to-Digital Conversion: Basic Concepts

Time Sampling: Basics

Data Banks


Basic Concepts


Data Functions and Transforms

Convolution, Correlation, and Covariance

Sampling Theory and Finite Data Considerations


Spectral Analysis: Classical Methods


The Fourier Transform: Fourier Series Analysis

Aperiodic Functions

MATLAB Implementation: Direct FFT

Truncated Fourier Analysis: Data Windowing

MATLAB Implementation: Window Functions

Power Spectrum

MATLAB Implementation: The Welch Method for

Power Spectral Density Determination


Digital Filters


The Z-Transform

Finite Impulse Response (FIR) Filters

Infinite Impulse Response (IIR) Filters


Spectral Analysis: Modern Techniques

Parametric Methods

Nonparametric Analysis: Eigenanalysis Frequency Estimation


Time–Frequency Analysis

Basic Approaches

Short-Term Fourier Transform: The Spectrogram

The Wigner-Ville Distribution: A Special Case of Cohen’s Class

The Choi-Williams and Other Distributions

MATLAB Implementation


Wavelet Analysis


The Continuous Wavelet Transform

The Discrete Wavelet Transform

Feature Detection: Wavelet Packets


Advanced Signal Processing Techniques: Optimal and Adaptive Filters

Optimal Signal Processing: Wiener Filters

Adaptive Signal Processing

Phase-Sensitive Detection


Multivariate Analyses: Principal Component Analysis and Independent Component Analysis

Introduction: Linear Transformations

Principal Component Analysis

Independent Component Analysis


Fundamentals of Imaging Processing: MATLAB Image Processing Toolbox

Image Processing Basics: MATLAB Image Formats

Image Display

Image Storage and Retrieval

Basic Arithmetic Operations

Advanced Protocols: Block Processing


Spectral Analysis: The Fourier Transform

The Two-Dimensional Fourier Transform

Linear Filtering

Spatial Transformations

Image Registration


Image Segmentation


Pixel-Based Methods

Continuity-Based Methods


Morphological Operations

Edge-Based Segmentation


Image Reconstruction


Magnetic Resonance Imaging

Functional MRI


Classification I: Linear Discriminant Analysis and Support Vector Machines


Linear Discriminators

Evaluating Classifier Performance

Higher Dimensions: Kernel Machines

Support Vector Machines

Machine Capacity: Overfitting or "Less Is More"

Cluster Analysis


Adaptive Neural Nets


McCullough-Pitts Neural Nets

The Gradient Descent Method or Delta Rule

Two-Layer Nets: Back-Projection

Three-Layer Nets

Training Strategies

Multiple Classifications

Multiple Input Variables


Annotated Bibliography

: AM

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