Biosignal and Medical Image Processing, Second Edition

Biosignal and Medical Image Processing, Second Edition

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ISBN 9781420062304
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Features

  • Provides a comprehensive presentation of the most valuable signal and image processing tools and techniques
  • Uses non-mathematical descriptions that help readers attain the practical knowledge necessary for correct application of these tools
  • Features extensive examples and problems in MATLAB that encourage exploration of the important properties of signal and image processing tools
  • Includes a CD-ROM with data, subroutines, and other supporting MATLAB code

Summary

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.

Table of Contents

Introduction

Typical Measurement Systems

Sources of Variability: Noise

Analog Filters: Filter Basics

Analog-to-Digital Conversion: Basic Concepts

Time Sampling: Basics

Data Banks

Problems

Basic Concepts

Noise

Data Functions and Transforms

Convolution, Correlation, and Covariance

Sampling Theory and Finite Data Considerations

Problems

Spectral Analysis: Classical Methods

Introduction

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

Problems

Digital Filters

Introduction

The Z-Transform

Finite Impulse Response (FIR) Filters

Infinite Impulse Response (IIR) Filters

Problems

Spectral Analysis: Modern Techniques

Parametric Methods

Nonparametric Analysis: Eigenanalysis Frequency Estimation

Problems

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

Problems

Wavelet Analysis

Introduction

The Continuous Wavelet Transform

The Discrete Wavelet Transform

Feature Detection: Wavelet Packets

Problems

Advanced Signal Processing Techniques: Optimal and Adaptive Filters

Optimal Signal Processing: Wiener Filters

Adaptive Signal Processing

Phase-Sensitive Detection

Problems

Multivariate Analyses: Principal Component Analysis and Independent Component Analysis

Introduction: Linear Transformations

Principal Component Analysis

Independent Component Analysis

Problems

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

Problems

Spectral Analysis: The Fourier Transform

The Two-Dimensional Fourier Transform

Linear Filtering

Spatial Transformations

Image Registration

Problems

Image Segmentation

Introduction

Pixel-Based Methods

Continuity-Based Methods

Multithresholding

Morphological Operations

Edge-Based Segmentation

Problems

Image Reconstruction

Introduction

Magnetic Resonance Imaging

Functional MRI

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"

Cluster Analysis

Problems

Adaptive Neural Nets

Introduction

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

Problems

Annotated Bibliography

: AM

Downloads / Updates

Resource OS Platform Updated Description Instructions
62301CD.zip Cross Platform June 22, 2012 CD Files

 
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