Biosignal and medical image processing

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Title:

Personal Author:

Semmlow, John L., author

Edition:

3rd ed.

Publication Information:

Boca Raton : CRC Press, Taylor & Francis Group, CRC Press is an imprint of the Taylor & Francis Group, an Informa business, 2014

Physical Description:

xxi, 607 p. : illustrations ; 26 cm.

ISBN:

9781466567368

Abstract:

"This third edition of a bestseller offers comprehensive coverage of the major approaches in biomedical signal and image processing. It provides a complete set of signal processing tools, including diagnostic decision-making tools, and classification methods. Thoroughly revised and updated, it supplies important new material on nonlinear methods for describing and classifying signals, including entropy-based methods and scaling methods. This edition covers data "cleaning" methods commonly used in such areas as heart rate variability studies, along with actual examples. It also includes new end-of-chapter problems"--Provided by publisher.

Subject Term:

Signal Processing, Computer-Assisted -- instrumentation

Biosensing Techniques

Image Interpretation, Computer-Assisted -- methods

Image Processing, Computer-Assisted -- methods

Added Author:

Griffel, Benjamin

Available:*

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... 1:FBME_L	32050000000722	R857.O6 S463 2014	Open Access Book	Book	Searching... Unknown
Searching... PSZ JB	30000010335124	R857.O6 S453 2014	Open Access Book	Book	Searching... Unknown

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Summary

Written specifically for biomedical engineers, Biosignal and Medical Image Processing, Third Edition provides a complete set of signal and image processing tools, including diagnostic decision-making tools, and classification methods. Thoroughly revised and updated, it supplies important new material on nonlinear methods for describing and classifying signals, including entropy-based methods and scaling methods. A full set of PowerPoint slides covering the material in each chapter and problem solutions is available to instructors for download.

See What's New in the Third Edition:

Two new chapters on nonlinear methods for describing and classifying signals. Additional examples with biological data such as EEG, ECG, respiration and heart rate variability Nearly double the number of end-of-chapter problems MATLAB® incorporated throughout the text Data "cleaning" methods commonly used in such areas as heart rate variability studies

The text provides a general understanding of image processing sufficient to allow intelligent application of the concepts, including a description of the underlying mathematical principals when needed. Throughout this textbook, signal and image processing concepts are implemented using the MATLAB® software package and several of its toolboxes.

The challenge of covering a broad range of topics at a useful, working depth is motivated by current trends in biomedical engineering education, 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. It is also suitable for an upper-level undergraduate course and would also be of value for students in other disciplines that would benefit from a working knowledge of signal and image processing.

;/LI> MATLAB® incorporated throughout the text Data "cleaning" methods commonly used in such areas as heart rate variability studies

re course. It is also suitable for an upper-level undergraduate course and would also be of value for students in other disciplines that would benefit from a working knowledge of signal and image processing.

Preface	p. xv
Acknowledgments	p. xix
Authors	p. xxi
Chapter 1 Introduction	p. 1
1.1 Biosignals	p. 1
1.2 Biosignal Measurement Systems	p. 3
1.3 Transducers	p. 4
1.4 Amplifier/Detector	p. 6
1.5 Analog Signal Processing and Filters	p. 7
1.5.1 Filter Types	p. 8
1.5.2 Filter Bandwidth	p. 9
1.5.3 Filter Order	p. 9
1.5.4 Filter Initial Sharpness	p. 12
1.6 ADC Conversion	p. 13
1.6.1 Amplitude Slicing	p. 15
1.6.2 Time Slicing	p. 18
1.6.3 Edge Effects	p. 22
1.6.4 Buffering and Real-Time Data Processing	p. 24
1.7 Data Banks	p. 24
1.8 Summary	p. 24
Problems	p. 25
Chapter 2 Biosignal Measurements, Noise, and Analysis	p. 29
2.1 Biosignals	p. 29
2.1.1 Signal Encoding	p. 30
2.1.2 Signal Linearity, Time Invariance, Causality	p. 31
2.1.2.1 Superposition	p. 32
2.1.3 Signal Basic Measurements	p. 33
2.1.4 Decibels	p. 37
2.1.5 Signal-to-Noise Ratio	p. 37
2.2 Noise	p. 38
2.2.1 Noise Sources	p. 39
2.2.2 Noise Properties: Distribution Functions	p. 40
2.2.3 Electronic Noise	p. 42
2.3 Signal Analysis: Data Functions and Transforms	p. 44
2.3.1 Comparing Waveforms	p. 45
2.3.1.1 Vector Representation	p. 46
2.3.1.2 Orthogonality	p. 48
2.3.1.3 Basis Functions	p. 50
2.3.2 Correlation-Based Analyses	p. 52
2.3.2.1 Correlation and Covariance	p. 53
2.3.2.2 Matrix of Correlations	p. 54
2.3.2.3 Cross-Correlation	p. 56
2.3.2.4 Autocorrelation	p. 59
2.3.2.5 Autocovariancc and Cross-Covariance	p. 62
2.3.3 Convolution and the Impulse Response	p. 64
2.4 Summary	p. 70
Problems	p. 71
Chapter 3 Spectral Analysis: Classical Methods	p. 77
3.1 Introduction	p. 77
3.2 Fourier Series Analysis	p. 79
3.2.1 Periodic Functions	p. 81
3.2.1.1 Symmetry	p. 88
3.2.2 Complex Representation	p. 88
3.2.3 Data Length and Spectral Resolution	p. 92
3.2.3.1 Aperiodic Functions	p. 94
3.2.4 Window Functions: Data Truncation	p. 95
3.3 Power Spectrum	p. 101
3.4 Spectral Averaging: Welch's Method	p. 105
3.5 Summary	p. 111
Problems	p. 111
Chapter 4 Noise Reduction and Digital Filters	p. 119
4.1 Noise Reduction	p. 119
4.2 Noise Reduction through Ensemble Averaging	p. 120
4.3 Z-Transform	p. 123
4.3.1 Digital Transfer Function	p. 124
4.4 Finite Impulse Response Filters	p. 127
4.4.1 FIR Filter Design and Implementation	p. 131
4.4.2 Derivative Filters: Two-Point Central Difference Algorithm	p. 141
4.4.2.1 Determining Cutoff Frequency and Skip Factor	p. 144
4.4.3 FIR Filter Design Using MATLAB	p. 145
4.5 Infinite Impulse Response Filters	p. 148
4.5.1 IIR Filter Implementation	p. 150
4.5.2 Designing IIR Filters with MATLAB	p. 151
4.6 Summary	p. 155
Problems	p. 155
Chapter 5 Modern Spectral Analysis: The Search for Narrowband Signals	p. 163
5.1 Parametric Methods	p. 163
5.1.1 Model Type and Model Order	p. 164
5.1.2 Autoregressive Model	p. 166
5.1.3 Yule-Walker Equations for the AR Model	p. 169
5.2 Nonparametric Analysis: Eigenanalysis Frequency Estimation	p. 174
5.2.1 Eigenvalue Decomposition Methods	p. 175
5.2.2 Determining Signal Subspace and Noise Subspace Dimensions	p. 176
5.2.3 MATLAB Implementation	p. 176
5.3 Summary	p. 185
Problems	p. 186
Chapter 6 Time-Frequency Analysis	p. 193
6.1 Basic Approaches	p. 193
6.2 The Short-Term Fourier Transform: The Spectrogram	p. 193
6.2.1 MATLAB Implementation of the STFT	p. 194
6.3 The Wigner-Ville Distribution: A Special Case of Cohen's Class	p. 200
6.3.1 The Instantaneous Autocorrelation Function	p. 200
6.3.2 Time-Frequency Distributions	p. 202
6.3.3 The Analytic Signal	p. 207
6.4 Cohen's Class Distributions	p. 208
6.4.1 The Choi-Williams Distribution	p. 208
6.5 Summary	p. 212
Problems	p. 214
Chapter 7 Wavelet Analysis	p. 217
7.1 Introduction	p. 217
7.2 Continuous Wavelet Transform	p. 218
7.2.1 Wavelet Time-Frequency Characteristics	p. 220
7.2.2 MATLAB Implementation	p. 222
7.3 Discrete Wavelet Transform	p. 225
7.3.1 Filter Banks	p. 226
7.3.1.1 Relationship between Analytical Expressions and Filter Banks	p. 230
7.3.2 MATLAB Implementation	p. 231
7.3.2.1 Denoising	p. 234
7.3.2.2 Discontinuity Detection	p. 236
7.4 Feature Detection: Wavelet Packets	p. 237
7.5 Summary	p. 243
Problems	p. 244
Chapter 8 Optimal and Adaptive Filters	p. 247
8.1 Optimal Signal Processing: Wiener Filters	p. 247
8.1.1 MATLAB Implementation	p. 250
8.2 Adaptive Signal Processing	p. 254
8.2.1 ALE and Adaptive Interference Suppression	p. 256
8.2.2 Adaptive Noise Cancellation	p. 257
8.2.3 MATLAB Implementation	p. 258
8.3 Phase-Sensitive Detection	p. 263
8.3.1 AM Modulation	p. 263
8.3.2 Phase-Sensitive Detectors	p. 265
8.3.3 MATLAB Implementation	p. 267
8.4 Summary	p. 269
Problems	p. 270
Chapter 9 Multivariate Analyses: Principal Component Analysis and Independent Component Analysis	p. 273
9.1 Introduction: Linear Transformations	p. 273
9.2 Principal Component Analysis	p. 276
9.2.1 Determination of Principal Components Using Singular-Value Decomposition	p. 278
9.2.2 Order Selection: The Scree Plot	p. 279
9.2.3 MATLAB Implementation	p. 279
9.2.3.1 Data Rotation	p. 279
9.2.4 PCA in MATLAB	p. 280
9.3 Independent Component Analysis	p. 284
9.3.1 MATLAB Implementation	p. 291
9.4 Summary	p. 293
Problems	p. 294
Chapter 10 Chaos and Nonlinear Dynamics	p. 297
10.1 Nonlinear Systems	p. 297
10.1.1 Chaotic Systems	p. 298
10.1.2 Types of Systems	p. 301
10.1.3 Types of Noise	p. 302
10.1.4 Chaotic Systems and Signals	p. 303
10.2 Phase Space	p. 304
10.2.1 Iterated Maps	p. 308
10.2.2 The Hénon Map	p. 308
10.2.3 Delay Space Embedding	p. 311
10.2.4 The Lorenz Attractor	p. 313
10.3 Estimating the Embedding Parameters	p. 315
10.3.1 Estimation of the Embedding Dimension Using Nearest Neighbors	p. 316
10.3.2 Embedding Dimension: SVD	p. 323
10.4 Quantifying Trajectories in Phase Space: The Lyapunov Exponent	p. 324
10.4.1 Goodness of Fit of a Linear Curve	p. 326
10.4.2 Methods of Determining the Lyapunov Exponent	p. 327
10.4.3 Estimating the Lyapunov Exponent Using Multiple Trajectories	p. 330
10.5 Nonlinear Analysis: The Correlation Dimension	p. 335
10.5.1 Fractal Objects	p. 335
10.5.2 The Correlation Sum	p. 337
10.6 Tests for Nonlinearity: Surrogate Data Analysis	p. 345
10.7 Summary	p. 353
Exercises	p. 353
Chapter 11 Nonlinearity Detection: Information-Based Methods	p. 357
11.1 Information and Regularity	p. 357
11.1.1 Shannon's Entropy Formulation	p. 358
11.2 Mutual Information Function	p. 359
11.2.1 Automutual Information Function	p. 364
11.3 Spectral Entropy	p. 370
11.4 Phase-Space-Based Entropy Methods	p. 374
11.4.1 Approximate Entropy	p. 374
11.4.2 Sample Entropy	p. 378
11.4.3 Coarse Graining	p. 381
11.5 Detrended Fluctuation Analysis	p. 385
11.6 Summary	p. 389
Problems	p. 390
Chapter 12 Fundamentals of Image Processing: The MATLAB Image Processing Toolbox	p. 393
12.1 Image-Processing Basics: MATLAB Image Formats	p. 393
12.1.1 General Image Formats: Image Array Indexing	p. 393
12.1.2 Image Classes: Intensity Coding Schemes	p. 395
12.1.3 Data Formats	p. 396
12.1.4 Data Conversions	p. 397
12.2 Image Display	p. 399
12.3 Image Storage and Retrieval	p. 404
12.4 Basic Arithmetic Operations	p. 404
12.5 Block-Processing Operations	p. 411
12.5.1 Sliding Neighborhood Operations	p. 411
12.5.2 Distinct Block Operations	p. 415
12.6 Summary	p. 416
Problems	p. 418
Chapter 13 Image Processing: Filters, Transformations, and Registration	p. 421
13.1 Two-Dimensional Fourier Transform	p. 421
13.1.1 MATLAB Implementation	p. 423
13.2 Linear Filtering	p. 425
13.2.1 MATLAB Implementation	p. 426
13.2.2 Filter Design	p. 427
13.3 Spatial Transformations	p. 433
13.3.1 Affine Transformations	p. 434
13.3.1.1 General Affine Transformations	p. 436
13.3.2 Projective Transformations	p. 437
13.4 Image Registration	p. 441
13.4.1 Unaided Image Registration	p. 442
13.4.2 Interactive Image Registration	p. 445
13.5 Summary	p. 447
Problems	p. 448
Chapter 14 Image Segmentation	p. 451
14.1 Introduction	p. 451
14.2 Pixel-Based Methods	p. 451
14.2.1 Threshold Level Adjustment	p. 452
14.2.2 MATLAB Implementation	p. 455
14.3 Continuity-Based Methods	p. 456
14.3.1 MATLAB Implementation	p. 457
14.4 Multithresholding	p. 463
14.5 Morphological Operations,	p. 465
14.5.1 MATLAB Implementation	p. 466
14.6 Edge-Based Segmentation	p. 472
14.6.1 Hough Transform	p. 473
14.6.2 MATLAB Implementation	p. 474
14.7 Summary	p. 477
Problems	p. 479
Chapter 15 Image Acquisition and Reconstruction	p. 483
15.1 Imaging Modalities	p. 483
15.2 CT, PET, and SPECT	p. 483
15.2.1 Radon Transform	p. 484
15.2.2 Filtered Back Projection	p. 486
15.2.3 Fan Beam Geometry	p. 489
15.2.4 MATLAB Implementation of the Forward and Inverse Radon Transforms: Parallel Beam Geometry	p. 489
15.2.5 MATLAB Implementation of the Forward and Inverse Radon Transforms: Fan Beam Geometry	p. 492
15.3 Magnetic Resonance Imaging	p. 494
15.3.1 Magnetic Gradients	p. 497
15.3.2 Data Acquisition: Pulse Sequences	p. 497
15.4 Functional MRI	p. 499
15.4.1 fMRI Implementation in MATLAB	p. 501
15.4.2 Principal Component and ICA	p. 503
15.5 Summary	p. 506
Problems	p. 508
Chapter 16 Classification I: Linear Discriminant Analysis and Support Vector Machines	p. 511
16.1 Introduction	p. 511
16.1.1 Classifier Design: Machine Capacity	p. 514
16.2 Linear Discriminators	p. 515
16.3 Evaluating Classifier Performance	p. 520
16.4 Higher Dimensions: Kernel Machines	p. 525
16.5 Support Vector Machines	p. 527
16.5.1 MATLAB Implementation	p. 530
16.6 Machine Capacity: Overfitting or "Less Is More"	p. 534
16.7 Extending the Number of Variables and Classes	p. 536
16.8 Cluster Analysis	p. 537
16.8.1 k-Nearest Neighbor Classifier	p. 538
16.8.2 k-Means Clustering Classifier	p. 540
16.9 Summary	p. 544
Problems	p. 545
Chapter 17 Classification II: Adaptive Neural Nets	p. 549
17.1 Introduction	p. 549
17.1.1 Neuron Models	p. 549
17.2 Training the McCullough-Pitts Neuron	p. 552
17.3 The Gradient Decent Method or Delta Rule	p. 557
17.4 Two-Layer Nets: Back Projection	p. 561
17.5 Three-Layer Nets	p. 565
17.6 Training Strategies	p. 568
17.6.1 Stopping Criteria: Cross-Validation	p. 568
17.6.2 Momentum	p. 569
17.7 Multiple Classifications	p. 573
17.8 Multiple Input Variables	p. 575
17.9 Summary	p. 577
Problems	p. 578
Appendix A Numerical Integration in MATLAB	p. 581
Appendix B Useful MATLAB Functions	p. 585
Bibliography	p. 589
Index	p. 593

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