Biosignal and medical image processing

Library	Item Barcode	Call Number	Material Type	Item Category 1	Status
Searching... PSZ JB	30000010196694	R857.O6 S453 2009	Open Access Book	Book	Searching... Unknown
Searching... PSZ JB	30000010237416	R857.O6 S453 2009	Open Access Book	Book	Searching... Unknown

Relying heavily on MATLAB® problems and examples, as well as simulated data, this text/reference surveys a vast array of signal and image processing tools for biomedical applications, providing a working knowledge of the technologies addressed while showcasing valuable implementation procedures, common pitfalls, and essential application concepts. The first and only textbook to supply a hands-on tutorial in biomedical signal and image processing, it offers a unique and proven approach to signal processing instruction, unlike any other competing source on the topic. The text is accompanied by a CD with support data files and software including all MATLAB examples and figures found in the text.

Preface	p. xi
Acknowledgments	p. xv
Author	p. xvii
Chapter 1 Introduction	p. 1
1.1 Typical Measurement Systems	p. 1
1.1.1 Transducers	p. 2
1.1.2 Further Study: The Transducer	p. 3
1.1.3 Analog Signal Processing	p. 4
1.2 Sources of Variability: Noise	p. 6
1.2.1 Electronic Noise	p. 8
1.2.2 Signal-to-Noise Ratio	p. 9
1.3 Analog Filters: Filter Basics	p. 10
1.3.1 Filter Types	p. 10
1.3.2 Filter Bandwidth	p. 11
1.3.3 Filter Order	p. 12
1.3.4 Filter Initial Sharpness	p. 12
1.4 Analog-to-Digital Conversion: Basic Concepts	p. 14
1.4.1 Analog-to-Digital Conversion Techniques	p. 15
1.4.2 Quantization Error	p. 16
1.4.3 Further Study: Successive Approximation Analog-to-Digital Conversion	p. 17
1.5 Time Sampling: Basics	p. 19
1.5.1 Further Study: Buffering and Real-Time Data Processing	p. 21
1.6 Data Banks	p. 22
Problems	p. 23
Chapter 2 Basic Concepts	p. 25
2.1 Noise	p. 25
2.1.1 Ensemble Averaging	p. 27
2.1.2 MATLAB Implementation	p. 28
2.2 Data Functions and Transforms	p. 29
2.2.1 Comparing Waveforms: Vector Representation	p. 30
2.2.2 Signal Analysis: Transformation and Basis Functions	p. 32
2.3 Convolution, Correlation, and Covariance	p. 35
2.3.1 Convolution and the Impulse Response	p. 35
2.3.2 Covariance and Correlation	p. 39
2.3.3 Covariance, Correlation, and Autocorrelation Matrices	p. 40
2.3.4 MATLAB Implementation	p. 42
2.4 Sampling Theory and Finite Data Considerations	p. 46
2.4.1 Edge Effects	p. 51
Problems	p. 53
Chapter 3 Spectral Analysis: Classical Methods	p. 55
3.1 Introduction	p. 55
3.2 Fourier Transform: Fourier Series Analysis	p. 57
3.2.1 Periodic Functions	p. 57
3.2.1.1 Symmetry	p. 60
3.2.2 Discrete-Time Fourier Analysis	p. 62
3.3 Aperiodic Functions	p. 65
3.3.1 Frequency Resolution	p. 66
3.4 MATLAB Implementation: Direct FFT	p. 67
3.5 Truncated Fourier Analysis: Data Windowing	p. 70
3.6 MATLAB Implementation: Window Functions	p. 73
3.7 Power Spectrum	p. 75
3.8 MATLAB Implementation: The Welch Method for Power Spectral Density Determination	p. 78
Problems	p. 81
Chapter 4 Digital Filters	p. 83
4.1 Introduction	p. 83
4.2 Z-Transform	p. 83
4.2.1 Digital Transfer Function	p. 84
4.2.2 MATLAB Implementation	p. 86
4.3 Finite Impulse Response (FIR) Filters	p. 88
4.3.1 FIR Filter Design	p. 89
4.3.2 Derivative Operation: The Two-Point Central Difference Algorithm	p. 93
4.3.3 MATLAB Implementation	p. 95
4.3.4 Filter Design and Application Using the MATLAB Signal Processing Toolbox	p. 98
4.3.4.1 Single-Stage FIR Filter Design	p. 99
4.3.4.2 Two-Stage FIR Filter Design	p. 100
4.4 Infinite Impulse Response (IIR) Filters	p. 106
4.4.1 MATLAB Implementation IIR Filters	p. 107
4.4.2 Single-Stage IIR Filter Design	p. 107
4.4.3 Two-Stage IIR Filter Design: Analog Style Filters	p. 109
Problems	p. 111
Chapter 5 Spectral Analysis: Modern Techniques	p. 115
5.1 Parametric Methods	p. 115
5.1.1 Yule-Walker Equations	p. 120
5.1.2 MATLAB Implementation	p. 122
5.2 Nonparametric Analysis: Eigenanalysis Frequency Estimation	p. 127
5.2.1 MATLAB Implementation	p. 128
Problems	p. 136
Chapter 6 Time-Frequency Analysis	p. 139
6.1 Basic Approaches	p. 139
6.2 Short-Term Fourier Transform: The Spectrogram	p. 139
6.2.1 MATLAB Implementation: The Short-Term Fourier Transform	p. 140
6.3 Wigner-Ville Distribution: A Special Case of Cohen's Class	p. 147
6.3.1 Instantaneous Autocorrelation Function	p. 147
6.4 Choi-Williams and Other Distributions	p. 152
6.4.1 Analytic Signal	p. 153
6.5 MATLAB Implementation	p. 154
6.5.1 Wigner-Ville Distribution	p. 154
6.5.2 Choi-Williams and Other Distributions	p. 157
Problems	p. 163
Chapter 7 Wavelet Analysis	p. 165
7.1 Introduction	p. 165
7.2 Continuous Wavelet Transform	p. 167
7.2.1 Wavelet Time-Frequency Characteristics	p. 168
7.2.2 MATLAB Implementation	p. 171
7.3 Discrete Wavelet Transform	p. 174
7.3.1 Filter Banks	p. 175
7.3.1.1 Relationship between Analytical Expressions and Filter Banks	p. 179
7.3.2 MATLAB Implementation	p. 180
7.3.2.1 Denoising	p. 185
7.3.2.2 Discontinuity Detection	p. 187
7.4 Feature Detection: Wavelet Packets	p. 189
Problems	p. 193
Chapter 8 Advanced Signal Processing Techniques: Optimal and Adaptive Filters	p. 195
8.1 Optimal Signal Processing: Wiener Filters	p. 195
8.1.1 MATLAB Implementation	p. 198
8.2 Adaptive Signal Processing	p. 202
8.2.1 Adaptive Line Enhancement (ALE) and Adaptive Interference Suppression	p. 205
8.2.2 Adaptive Noise Cancellation (ANC)	p. 206
8.2.3 MATLAB Implementation	p. 207
8.3 Phase-Sensitive Detection	p. 213
8.3.1 AM Modulation	p. 213
8.3.2 Phase-Sensitive Detectors	p. 215
8.3.3 MATLAB Implementation	p. 218
Problems	p. 220
Chapter 9 Multivariate Analyses: Principal Component Analysis and Independent Component Analysis	p. 223
9.1 Introduction: Linear Transformations	p. 223
9.2 Principal Component Analysis	p. 226
9.2.1 Determination of Principal Components Using Singular Value Decomposition	p. 229
9.2.2 Order Selection: The Scree Plot	p. 230
9.2.3 MATLAB Implementation	p. 230
9.2.3.1 Data Rotation	p. 230
9.2.4 PCA Evaluation	p. 232
9.3 Independent Component Analysis	p. 236
9.3.1 MATLAB Implementation	p. 241
Problems	p. 245
Chapter 10 Fundamentals of Image Processing: MATLAB Image Processing Toolbox	p. 247
10.1 Image Processing Basics: MATLAB Image Formats	p. 247
10.1.1 General Image Formats: Image Array Indexing	p. 247
10.1.2 Data Classes: Intensity Coding Schemes	p. 248
10.1.3 Data Formats	p. 250
10.1.4 Data Conversions	p. 250
10.2 Image Display	p. 253
10.3 Image Storage and Retrieval	p. 257
10.4 Basic Arithmetic Operations	p. 258
10.5 Advanced Protocols: Block Processing	p. 264
10.5.1 Sliding Neighborhood Operations	p. 264
10.5.2 Distinct Block Operations	p. 268
Problems	p. 272
Chapter 11 Spectral Analysis: The Fourier Transform	p. 275
11.1 Two-Dimensional Fourier Transform	p. 275
11.1.1 MATLAB Implementation	p. 276
11.2 Linear Filtering	p. 279
11.2.1 MATLAB Implementation	p. 280
11.2.2 Filter Design	p. 281
11.3 Spatial Transformations	p. 286
11.3.1 MATLAB Implementation	p. 288
11.3.1.1 Affine Transformations	p. 288
11.3.1.2 General Affine Transformations	p. 290
11.3.1.3 Projective Transformations	p. 292
11.4 Image Registration	p. 296
11.4.1 Unaided Image Registration	p. 297
11.4.2 Interactive Image Registration	p. 300
Problems	p. 302
Chapter 12 Image Segmentation	p. 305
12.1 Introduction	p. 305
12.2 Pixel-Based Methods	p. 305
12.2.1 Threshold Level Adjustment	p. 306
12.2.2 MATLAB Implementation	p. 309
12.3 Continuity-Based Methods	p. 311
12.3.1 MATLAB Implementation	p. 312
12.4 Multithresholding	p. 317
12.5 Morphological Operations	p. 319
12.5.1 MATLAB Implementation	p. 321
12.6 Edge-Based Segmentation	p. 326
12.6.1 Hough Transform	p. 327
12.6.2 MATLAB Implementation	p. 328
Problems	p. 332
Chapter 13 Image Reconstruction	p. 335
13.1 Introduction	p. 335
13.1.1 CT, PET, SPECT	p. 335
13.1.2 Filtered Back-Projection	p. 339
13.1.3 Fan Beam Geometry	p. 341
13.1.4 MATLAB Implementation	p. 342
13.1.4.1 Radon Transform	p. 342
13.1.4.2 Inverse Radon Transform: Parallel Beam Geometry	p. 342
13.1.4.3 Radon and Inverse Radon Transform: Fan Beam Geometry	p. 344
13.2 Magnetic Resonance Imaging	p. 346
13.2.1 Basic Principles	p. 346
13.2.2 Data Acquisition: Pulse Sequences	p. 349
13.3 Functional MRI	p. 351
13.3.1 MATLAB Implementation	p. 352
13.3.2 Principal Component and Independent Component Analyses	p. 354
Problems	p. 359
Chapter 14 Classification I: Linear Discriminant Analysis and Support Vector Machines	p. 361
14.1 Introduction	p. 361
14.1.1 Classifier Design	p. 364
14.2 Linear Discriminators	p. 365
14.3 Evaluating Classifier Performance	p. 371
14.4 Higher Dimensions: Kernel Machines	p. 376
14.5 Support Vector Machines	p. 378
14.5.1 MATLAB Implementation	p. 381
14.6 Machine Capacity: Overfitting or "Less Is More"	p. 385
14.7 Cluster Analysis	p. 389
14.7.1 The k-Nearest Neighbor Classifier	p. 389
14.7.2 The k-Means Clustering Classifier	p. 391
Problems	p. 396
Chapter 15 Adaptive Neural Nets	p. 399
15.1 Introduction	p. 399
15.1.1 Neuron Models	p. 399
15.2 McCullough-Pitts Neural Nets	p. 403
15.3 Gradient Descent Method or Delta Rule	p. 407
15.4 Two-Layer Nets: Backpropagation	p. 411
15.5 Three-Layer Nets	p. 416
15.6 Training Strategies	p. 419
15.6.1 Stopping Criteria: Cross-Validation	p. 419
15.6.2 Momentum	p. 420
15.7 Multiple Classifications	p. 426
15.8 Multiple Input Variables	p. 428
Problems	p. 429
Annotated Bibliography	p. 433
Index	p. 437

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