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Biosignal and Medical Image Processing, Second Edition 2nd New edition [Kõva köide]

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(Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA)
  • Formaat: Hardback, 448 pages, kõrgus x laius: 229x152 mm, kaal: 998 g, 173 equations; 186 Halftones, black and white; 6 Tables, black and white
  • Ilmumisaeg: 24-Oct-2008
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1420062301
  • ISBN-13: 9781420062304
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Biosignal and Medical Image Processing, Second Edition 2nd New editionSuurem pilt
  • Formaat: Hardback, 448 pages, kõrgus x laius: 229x152 mm, kaal: 998 g, 173 equations; 186 Halftones, black and white; 6 Tables, black and white
  • Ilmumisaeg: 24-Oct-2008
  • Kirjastus: CRC Press Inc
  • ISBN-10: 1420062301
  • ISBN-13: 9781420062304
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781420062304.html
Märksõnad:
Semmlow (surgery, U. of Medicine and Dentistry of New Jersey and biomedical engineering, Rutgers U.) offers a textbook for a one-semester course in signal processing or a course combining signal and imaging processing, for graduate or advanced undergraduate students of biomedical engineering who may have no understanding of how the underlying mathematics of the processing works. Among the topics are basic concepts, classical methods and modern techniques for spectral analysis, time-frequency analysis, optimal and adaptive filters, multivariate analyses, the Fourier transform, image segmentation and reconstruction, and adaptive neural nets. He uses MATLAB for examples. No date is mentioned for the first edition. Two chapters on classical methods have been added to the second. Annotation ©2010 Book News, Inc., Portland, OR (booknews.com)
Preface xi
Acknowledgments xv
Author xvii
Introduction
1(24)
Typical measurement Systems
1(5)
Transducers
2(1)
Further Study: The Transducer
3(1)
Analog Signal Processing
4(2)
Sources of Variability: Noise
6(4)
Electronic Noise
8(1)
Signal-to-Noise Ratio
9(1)
Analog Filters: Filter Basics
10(4)
Filter Types
10(1)
Filter Bandwidth
11(1)
Filter Order
12(1)
Filter Initial Sharpness
12(2)
Analog-to-Digital Conversion: Basic Concepts
14(5)
Analog-to-Digital Conversion Techniques
15(1)
Quantization Error
16(1)
Further Study: Successive Approximation Analog-to-Digital Conversion
17(2)
Time Sampling: Basics
19(3)
Further Study: Buffering and Real-Time Data Processing
21(1)
Data Banks
22(3)
Problems
23(2)
Basic Concepts
25(30)
Noise
25(4)
Ensemble Averaging
27(1)
MATLAB®Implementation
28(1)
Data Functions and Transforms
29(6)
Comparing Waveforms: Vector Representation
30(2)
Signal Analysis: Transformation and Basis Functions
32(3)
Convolution, Correlation, and Covariance
35(11)
Convolution and the Impulse Response
35(4)
Covariance and Correlation
39(1)
Covariance, Correlation, and Autocorrelation Matrices
40(2)
MATLAB® Implementation
42(4)
Sampling Theory and Finite Data considerations
46(9)
Edge Effects
51(2)
Problems
53(2)
Spectral Analysis: Classical Methods
55(28)
Introduction
55(2)
Fourier Transform: Fourier Series Analysis
57(8)
Periodic Functions
57(3)
Symmetry
60(2)
Discrete-Time Fourier Analysis
62(3)
Aperiodic Functions
65(2)
Frequency Resolution
66(1)
MATLAB® Implementation: Direct FFT
67(3)
Truncated Fourier Analysis: Data Windowing
70(3)
MATLAB® Implementation: Window Functions
73(2)
Power Spectrum
75(3)
MATLAB® Implementation: The Welch Method for Power Spectral Density Determination
78(5)
Problems
81(2)
Digital Filters
83(32)
Introduction
83(1)
Z-Transform
83(5)
Digital Transfer Function
84(2)
MATLAB® Implementation
86(2)
Finite Impulse Response (FIR) Filters
88(18)
FIR Filter Design
89(4)
Derivative Operation: The Two-Point Central Difference Algorithm
93(2)
MATLAB® Implementation
95(3)
Filter Design and Application Using the MATLAB® Signal Processing Toolbox
98(1)
Single-Stage FIR Filter Design
99(1)
Two-Stage FIR Filter Design
100(6)
Infinite Impulse Response (IIR) Filters
106(9)
MATLAB® Implementation IIR Filters
107(1)
Single-Stage IIR Filter Design
107(2)
Two-Stage IIR Filter Design: Analog Style Filters
109(2)
Problems
111(4)
Spectral Analysis: Modern Techniques
115(24)
Parametric Methods
115(12)
Yule-Walker Equations
120(2)
MATLAB® Implementation
122(5)
Nonparametric Analysis: Eigenanalysis Frequency Estimation
127(12)
MATLAB® Implementation
128(8)
Problems
136(3)
Time-Frequency Analysis
139(26)
Basic Approaches
139(1)
Short-Term Fourier Transform: The Spectrogram
139(8)
MATLAB® Implementation: The Short-Term Fourier Transform
140(7)
Wigner-Ville Distribution: A Special Case of Cohen's Class
147(5)
Instantaneous Autocorrelation Function
147(5)
Choi-Williams and Other Distributions
152(2)
Analytic Signal
153(1)
MATLAB® Implementation
154(11)
Wigner-Ville Distribution
154(3)
Choi-Williams and Other Distributions
157(6)
Problems
163(2)
Wavelet Analysis
165(30)
Introduction
165(2)
Continuous Wavelet Transform
167(7)
Wavelet Time-Frequency Characteristics
168(3)
MATLAB® Implementation
171(3)
Discrete Wavelet Transform
174(15)
Filter Banks
175(4)
Relationship between Analytical Expressions and Filter Banks
179(1)
MATLAB® Implementation
180(5)
Denoising
185(2)
Discontinuity Detection
187(2)
Feature Detection: Wavelet Packets
189(6)
Problems
193(2)
Advanced Signal Processing Techniques: Optimal and Adaptive Filters
195(28)
Optimal Signal Processing: Wiener Filters
195(7)
MATLAB® Implementation
198(4)
Adaptive Signal Processing
202(11)
Adaptive Line Enhancement (ALE) and Adaptive Interference Suppression
205(1)
Adaptive Noise Cancellation (ANC)
206(1)
MATLAB® Implementation
207(6)
Phase-Sensitive Detectors
213(10)
AM Modulation
213(2)
Phase-Sensitive Detectors
215(3)
MATLAB® Implementation
218(2)
Problems
220(3)
Multivariate Analyses: Principal Component Analysis and Independent Component Analysis
223(24)
Introduction: Linear Transformations
223(3)
Principal Component Analysis
226(10)
Determination of Principal Components Using Singular Value Decomposition
229(1)
Order Selection: The Scree Plot
230(1)
MATLAB® Implementation
230(1)
Data Rotation
230(2)
PCA Evaluation
232(4)
Independent Component Analysis
236(11)
MATLAB® Implementation
241(4)
Problems
245(2)
Fundamentals of Image Processing: MATLAB® Image Processing Toolbox
247(28)
Image Processing Basics; MATLAB® Image Formats
247(6)
General Image Formats: Image Array Indexing
247(1)
Data Classes: Intensity Coding Schemes
248(2)
Data Formats
250(1)
Data Conversions
250(3)
Image Display
253(4)
Image Storage and Retrieval
257(1)
Basic Arithmetic Operations
258(6)
Advanced Protocols: Block Processing
264(11)
Sliding Neighborhood Operations
264(4)
Distinct Block Operations
268(4)
Problems
272(3)
Spectral Analysis: The Fourier Transform
275(30)
Two-Dimensional Fourier Transform
275(4)
MATLAB® Implementation
276(3)
Linear Filtering
279(7)
MATLAB® Implmentation
280(1)
Filter Design
281(5)
Spatial Transformations
286(10)
MATLAB® Implementation
288(1)
Affine Transformations
288(2)
General Affine Transformations
290(2)
Projective Transformations
292(4)
Image Registration
296(9)
Unaided Image Registration
297(3)
Interactive Image Registration
300(2)
Problems
302(3)
Image Segmentation
305(30)
Introduction
305(1)
Pixel-Based Methods
305(6)
Threshold Level Adjustment
306(3)
MATLAB® Implementation
309(2)
Continuity-Based Methods
311(6)
MATLAB® Implementation
312(5)
Multithresholding
317(2)
Morphological Operations
319(7)
MATLAB® Implementation
321(5)
Edge-Based Segmentation
326(9)
Hough Transform
327(1)
MATLAB® Implementation
328(4)
Problems
332(3)
Image Reconstruction
335(26)
Introduction
335(11)
CT, PET, SPECT
335(4)
Filtered Back-Projection
339(2)
Fan Beam Geometry
341(1)
MATLAB® Implementation
342(1)
Radon Transform
342(1)
Inverse Radon Transform: Parallel Beam Geometry
342(2)
Radon and Inverse Radon Transform: Fan Beam Geometry
344(2)
Magnetic Resonance Imaging
346(5)
Basic Principles
346(3)
Data Acquisition: Pulse Sequences
349(2)
Functional MRI
351(10)
MATLAB® Implementation
352(2)
Principal Component and Independent Component Analyses
354(5)
Problems
359(2)
Classification I: Linear Discriminant Analysis and Support Vector Machines
361(38)
Introduction
361(4)
Classifier Design
364(1)
Linear Discriminators
365(6)
Evaluating Classifier Performance
371(5)
Higher Dimensions: Kernel Machines
376(2)
Support Vector Machines
378(7)
MATLAB® Implementation
381(4)
Machine Capacity: Overfitting or ``Less Is More''
385(4)
Cluster Analysis
389(10)
The k-Nearest Neighbor Classifier
389(2)
The k-Means Clustering Classifier
391(5)
Problems
396(3)
Adaptive Neural Nets
399(34)
Introduction
399(4)
Neuron Models
399(4)
McCullough-Pitts Neural Nets
403(4)
Gradient Descent Method or Delta Rule
407(4)
Two-Layer Nets: Backpropagation
411(5)
Three-Layer Nets
416(3)
Training Strategies
419(7)
Stopping Criteria: Cross-Validation
419(1)
Momentum
420(6)
Multiple Classifications
426(2)
Multiple Input Variables
428(5)
Problems
429(4)
Annotated Bibliography 433(4)
Index 437
Rutgers University, Piscataway, New Jersey, USA