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El. knyga: Biomedical Signal and Image Processing

2.86/5 (8 ratings by Goodreads)
(University of North Carolina at Charlotte, USA), (Virginia Commonwealth University, Richmond, USA)
  • Formatas: 411 pages
  • Išleidimo metai: 19-Apr-2016
  • Leidėjas: CRC Press Inc
  • ISBN-13: 9781000218817
Kitos knygos pagal šią temą:
Biomedical Signal and Image ProcessingDidesnis paveikslėlis
  • Formatas: 411 pages
  • Išleidimo metai: 19-Apr-2016
  • Leidėjas: CRC Press Inc
  • ISBN-13: 9781000218817
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First published in 2005, Biomedical Signal and Image Processing received wide and welcome reception from universities and industry research institutions alike, offering detailed, yet accessible information at the reference, upper undergraduate, and first year graduate level. Retaining all of the quality and precision of the first edition, Biomedical Signal and Image Processing, Second Edition offers a number of revisions and improvements to provide the most up-to-date reference available on the fundamental signal and image processing techniques that are used to process biomedical information.

Addressing the application of standard and novel processing techniques to some of todays principle biomedical signals and images over three sections, the book begins with an introduction to digital signal and image processing, including Fourier transform, image filtering, edge detection, and wavelet transform. The second section investigates specifically biomedical signals, such as ECG, EEG, and EMG, while the third focuses on imaging using CT, X-Ray, MRI, ultrasound, positron, and other biomedical imaging techniques.

Updated and expanded, Biomedical Signal and Image Processing, Second Edition offers numerous additional, predominantly MATLAB, examples to all chapters to illustrate the concepts described in the text and ensure a complete understanding of the material. The author takes great care to clarify ambiguities in some mathematical equations and to further explain and justify the more complex signal and image processing concepts to offer a complete and understandable approach to complicated concepts.

Recenzijos

"This is a great book, ideal for a biomedical signal and image processing course. a great introduction to the topic, while it also includes advanced topics in the field for graduate courses. a great collection of topics [ including] introduction to signal and image processing, advanced signal processing techniques, physiology and specific signal processing techniques used for various biomedical signals, an introduction to medical image formation, and advanced topics on medical image processing. This is a great book, highly recommended to any research and educator in this field."

Purang Abolmaesumi, Department of Electrical Engineering, University of British Columbia, Vancouver, Canada

"I am not aware of a book that covers both biomedical image processing and biomedical signal processing. There are a large number of books that focus on the physics of biomedical imaging. This book however, balances the coverage of physics of biomedical imaging with biomedical image processing. I was impressed by how clearly the concepts are explained. The authors ability to introduce concepts at the level appropriate for senior undergraduate or graduate level students is impressive. programming questions at the end of the chapter that will give the reader the opportunity to try out the signal processing techniques introduced in the chapter. Also, sample signals are included on the accompanying CD. I would definitely use this book as my textbook in a biomedical signal-processing course."

Shahram Shirani, McMaster University, Hamilton, Ontario, Canada

" an excellent bridge between the physiological significance of the methods, and the underlying mathematics. covers a broad range of biomedical problems. clear, well written, and easy to follow. this is an excellent course textbook, and also an excellent reference book for professionals."

Dr. Kristian Sandberg, Computational Solutions, Inc., Boulder, Colorado, USA "This is a great book, ideal for a biomedical signal and image processing course. a great introduction to the topic, while it also includes advanced topics in the field for graduate courses. a great collection of topics [ including] introduction to signal and image processing, advanced signal processing techniques, physiology and specific signal processing techniques used for various biomedical signals, an introduction to medical image formation, and advanced topics on medical image processing. This is a great book, highly recommended to any research and educator in this field."

Purang Abolmaesumi, Department of Electrical Engineering, University of British Columbia, Vancouver, Canada

"I am not aware of a book that covers both biomedical image processing and biomedical signal processing. There are a large number of books that focus on the physics of biomedical imaging. This book however, balances the coverage of physics of biomedical imaging with biomedical image processing. I was impressed by how clearly the concepts are explained. The authors ability to introduce concepts at the level appropriate for senior undergraduate or graduate level students is impressive. programming questions at the end of the chapter that will give the reader the opportunity to try out the signal processing techniques introduced in the chapter. Also, sample signals are included on the accompanying CD. I would definitely use this book as my textbook in a biomedical signal-processing course."

Shahram Shirani, McMaster University, Hamilton, Ontario, Canada

" an excellent bridge between the physiological significance of the methods, and the underlying mathematics. covers a broad range of biomedical problems. clear, well written, and easy to follow. this is an excellent course textbook, and also an excellent reference book for professionals."

Dr. Kristian Sandberg, Computational Solutions, Inc., Boulder, Colorado, USA

Preface xvii
Acknowledgments xix
Introduction xxi
PART I Introduction to Digital Signal and Image Processing
Chapter 1 Signals and Biomedical Signal Processing
3(12)
1.1 Introduction and Overview
3(1)
1.2 What Is a "Signal"?
3(1)
1.3 Analog, Discrete, and Digital Signals
4(3)
1.3.1 Analog Signals
4(1)
1.3.2 Discrete Signals
4(2)
1.3.3 Digital Signals
6(1)
1.4 Processing and Transformation of Signals
7(1)
1.5 Signal Processing for Feature Extraction
8(1)
1.6 Some Characteristics of Digital Images
9(4)
1.6.1 Image Capturing
9(1)
1.6.2 Image Representation
9(2)
1.6.3 Image Histogram
11(2)
1.7 Summary
13(2)
Problems
13(2)
Chapter 2 Fourier Transform
15(24)
2.1 Introduction and Overview
15(1)
2.2 One-Dimensional Continuous Fourier Transform
15(11)
2.2.1 Properties of One-Dimensional Fourier Transform
22(1)
2.2.1.1 Signal Shift
23(1)
2.2.1.2 Convolution
23(1)
2.2.1.3 Linear Systems Analysis
24(2)
2.2.1.4 Differentiation
26(1)
2.2.1.5 Scaling Property
26(1)
2.3 Sampling and Nyquist Rate
26(1)
2.4 One-Dimensional Discrete Fourier Transform
27(4)
2.4.1 Properties of DFT
28(3)
2.5 Two-Dimensional Discrete Fourier Transform
31(2)
2.6 Filter Design
33(3)
2.7 Summary
36(3)
Problems
36(3)
Chapter 3 Image Filtering, Enhancement, and Restoration
39(24)
3.1 Introduction and Overview
39(1)
3.2 Point Processing
40(7)
3.2.1 Contrast Enhancement
41(2)
3.2.2 Bit-Level Slicing
43(1)
3.2.3 Histogram Equalization
44(3)
3.3 Mask Processing: Linear Filtering in Space Domain
47(11)
3.3.1 Low-Pass Filters
48(2)
3.3.2 Median Filters
50(3)
3.3.3 Sharpening Spatial Filters
53(1)
3.3.3.1 High-Pass Filters
53(1)
3.3.3.2 High-Boost Filters
54(2)
3.3.3.3 Derivative Filters
56(2)
3.4 Frequency-Domain Filtering
58(3)
3.4.1 Smoothing Filters in Frequency Domain
59(1)
3.4.1.1 Ideal Low-Pass Filter
59(1)
3.4.1.2 Butterworth Low-Pass Filters
60(1)
3.4.2 Sharpening Filters in Frequency Domain
60(1)
3.4.2.1 Ideal High-Pass Filters
60(1)
3.4.2.2 Butterworth High-Pass Filters
61(1)
3.5 Summary
61(2)
Problems
61(1)
Reference
62(1)
Chapter 4 Edge Detection and Segmentation of Images
63(16)
4.1 Introduction and Overview
63(1)
4.2 Edge Detection
63(6)
4.2.1 Sobel Edge Detection
63(3)
4.2.2 Laplacian of Gaussian Edge Detection
66(1)
4.2.3 Canny Edge Detection
67(2)
4.3 Image Segmentation
69(8)
4.3.1 Point Detection
70(1)
4.3.2 Line Detection
71(1)
4.3.3 Region and Object Segmentation
72(1)
4.3.3.1 Region Segmentation Using Luminance Thresholding
73(2)
4.3.3.2 Region Growing
75(1)
4.3.3.3 Quad-Trees
76(1)
4.4 Summary
77(2)
Problems
77(2)
Chapter 5 Wavelet Transform
79(22)
5.1 Introduction and Overview
79(1)
5.2 From FT to STFT
79(7)
5.3 One-Dimensional Continuous Wavelet Transform
86(2)
5.4 One-Dimensional Discrete Wavelet Transform
88(6)
5.4.1 Discrete Wavelet Transform on Discrete Signals
90(4)
5.5 Two-Dimensional Wavelet Transform
94(2)
5.5.1 Two-Dimensional Discrete Wavelet Transform
94(2)
5.6 Main Applications of DWT
96(3)
5.6.1 Filtering and Denoising
96(2)
5.6.2 Compression
98(1)
5.7 Discrete Wavelet Transform in MATLAB®
99(1)
5.8 Summary
99(2)
Problems
99(2)
Chapter 6 Other Signal and Image Processing Methods
101(24)
6.1 Introduction and Overview
101(1)
6.2 Complexity Analysis
101(3)
6.2.1 Signal Complexity and Signal Mobility
101(1)
6.2.2 Fractal Dimension
102(1)
6.2.3 Wavelet Measures
103(1)
6.2.4 Entropy
104(1)
6.3 Cosine Transform
104(3)
6.4 Introduction to Stochastic Processes
107(7)
6.4.1 Statistical Measures for Stochastic Processes
107(2)
6.4.2 Stationary and Ergodic Stochastic Processes
109(2)
6.4.3 Correlation Functions and Power Spectra
111(3)
6.5 Introduction to Information Theory
114(4)
6.5.1 Entropy
114(2)
6.5.2 Data Representation and Coding
116(1)
6.5.3 Hoffman Coding
117(1)
6.6 Registration of Images
118(3)
6.7 Summary
121(4)
Problems
122(3)
Chapter 7 Clustering and Classification
125(30)
7.1 Introduction and Overview
125(1)
7.2 Clustering versus Classification
125(2)
7.3 Feature Extraction
127(4)
7.3.1 Biomedical and Biological Features
128(1)
7.3.2 Signal and Image Processing Features
128(1)
7.3.2.1 Signal Power in Frequency Bands
128(1)
7.3.2.2 Wavelet Measures
129(1)
7.3.2.3 Complexity Measures
129(1)
7.3.2.4 Geometric Measures
129(2)
7.4 K-Means: A Simple Clustering Method
131(3)
7.5 Bayesian Classifier
134(4)
7.5.1 Loss Function
136(2)
7.6 Maximum Likelihood Method
138(2)
7.7 Neural Networks
140(10)
7.7.1 Perceptron
140(5)
7.7.2 Sigmoid Neural Networks
145(1)
7.7.2.1 Activation Function
146(1)
7.7.2.2 Backpropagation Algorithm
147(1)
7.7.2.3 Momentum
148(1)
7.7.3 MATLAB® for Neural Networks
149(1)
7.8 Summary
150(5)
Problems
150(2)
Reference
152(3)
PART II Processing of Biomedical Signals
Chapter 8 Electric Activities of the Cell
155(16)
8.1 Introduction and Overview
155(1)
8.2 Ion Transport in Biological Cells
155(5)
8.2.1 Transmembrane Potential
156(4)
8.3 Electric Characteristics of Cell Membrane
160(4)
8.3.1 Membrane Resistance
160(1)
8.3.2 Membrane Capacitance
160(1)
8.3.3 Cell Membrane's Equivalent Electric Circuit
161(1)
8.3.4 Action Potential
161(3)
8.4 Hodgkin--Huxley Model
164(2)
8.5 Electric Data Acquisition
166(2)
8.5.1 Propagation of Electric Potential as a Wave
167(1)
8.6 Some Practical Considerations on Biomedical Electrodes
168(1)
8.7 Summary
169(2)
Problems
169(2)
Chapter 9 Electrocardiogram
171(26)
9.1 Introduction and Overview
171(1)
9.2 Function and Structure of the Heart
171(5)
9.2.1 Cardiac Muscle
173(1)
9.2.2 Cardiac Excitation Process
174(2)
9.3 Electrocardiogram: Signal of Cardiovascular System
176(6)
9.3.1 Origin of ECG
176(2)
9.3.2 ECG Electrode Placement
178(2)
9.3.3 Modeling and Representation of ECG
180(1)
9.3.4 Periodicity of ECG: Heart Rate
181(1)
9.4 Cardiovascular Diseases and ECG
182(8)
9.4.1 Atrial Fibrillation
182(1)
9.4.2 Ventricular Arrhythmias
183(1)
9.4.3 Ventricular Tachycardia
184(1)
9.4.4 Ventricular Fibrillation
184(1)
9.4.5 Myocardial Infarction
184(1)
9.4.6 Atrial Flutter
185(1)
9.4.7 Cardiac Reentry
185(1)
9.4.8 Atrioventricular Block
186(1)
9.4.8.1 Main Types of AV Block
186(2)
9.4.9 Wolf--Parkinson--White Syndrome
188(1)
9.4.10 Extrasystole
189(1)
9.5 Processing and Feature Extraction of ECG
190(3)
9.5.1 Time-Domain Analysis
191(1)
9.5.2 Frequency-Domain Analysis
191(2)
9.5.3 Wavelet-Domain Analysis
193(1)
9.6 Summary
193(4)
Problems
194(3)
Chapter 10 Electroencephalogram
197(20)
10.1 Introduction and Overview
197(1)
10.2 Brain and Its Functions
197(2)
10.3 Electroencephalogram: Signal of the Brain
199(4)
10.3.1 EEG Frequency Spectrum
201(1)
10.3.2 Significance of EEG
202(1)
10.4 Evoked Potentials
203(3)
10.4.1 Auditory-Evoked Potentials
203(1)
10.4.2 Somatosensory-Evoked Potentials
204(1)
10.4.3 Visual-Evoked Potentials
204(1)
10.4.4 Event-Related Potentials
205(1)
10.5 Diseases of Central Nervous System and EEG
206(3)
10.5.1 Epilepsy
206(2)
10.5.2 Sleep Disorders
208(1)
10.5.3 Brain Tumor
209(1)
10.5.4 Other Diseases
209(1)
10.6 EEG for Assessment of Anesthesia
209(1)
10.7 Processing and Feature Extraction of EEG
210(4)
10.7.1 Sources of Noise on EEG
210(1)
10.7.2 Frequency-Domain Analysis
211(1)
10.7.3 Time-Domain Analysis
212(1)
10.7.3.1 Coherence Analysis
213(1)
10.7.4 Wavelet-Domain Analysis
214(1)
10.8 Summary
214(3)
Problems
215(2)
Chapter 11 Electromyogram
217(20)
11.1 Introduction and Overview
217(1)
11.2 Muscle
217(6)
11.2.1 Motor Unit
218(2)
11.2.2 Muscle Contraction
220(1)
11.2.3 Muscle Force
221(2)
11.3 EMG: Signal of Muscles
223(3)
11.3.1 Significance of EMG
225(1)
11.4 Neuromuscular Diseases and EMG
226(3)
11.4.1 Abnormal Enervation
226(1)
11.4.2 Pathological Motor Units
227(1)
11.4.3 Abnormal Neuromuscular Transmission in Motor Units
228(1)
11.4.4 Defects in Muscle Cell Membrane
229(1)
11.5 Other Applications of EMG
229(1)
11.6 Processing and Feature Extraction of EMG
230(3)
11.6.1 Sources of Noise on EMG
230(1)
11.6.2 Time-Domain Analysis
231(1)
11.6.3 Frequency- and Wavelet-Domain Analysis
232(1)
11.7 Summary
233(4)
Acknowledgment
233(1)
Problems
233(4)
Chapter 12 Other Biomedical Signals
237(12)
12.1 Introduction and Overview
237(1)
12.2 Blood Pressure and Blood Flow
237(1)
12.3 Electrooculogram
238(3)
12.4 Magnetoencephalogram
241(1)
12.5 Respiratory Signals
242(2)
12.6 More Biomedical Signals
244(1)
12.7 Summary
245(4)
Problems
245(1)
Reference
245(4)
PART III Processing of Biomedical Images
Chapter 13 Principles of Computed Tomography
249(12)
13.1 Introduction and Overview
249(4)
13.1.1 Attenuation Tomography
250(1)
13.1.2 Time-of-Flight Tomography
251(1)
13.1.3 Reflection Tomography
251(1)
13.1.4 Diffraction Tomography
252(1)
13.2 Formulation of Attenuation Computed Tomography
253(5)
13.2.1 Attenuation Tomography
255(3)
13.3 Fourier Slice Theorem
258(2)
13.4 Summary
260(1)
Problems
260(1)
Chapter 14 X-Ray Imaging and Computed Tomography
261(22)
14.1 Introduction and Overview
261(1)
14.2 Physics of X-Ray
261(5)
14.2.1 Imaging with X-Ray
264(1)
14.2.2 Radiation Dose
265(1)
14.3 Attenuation-Based X-Ray Imaging
266(1)
14.4 X-Ray Detection
267(4)
14.5 Image Quality
271(1)
14.6 Computed Tomography
272(2)
14.7 Biomedical CT Scanners
274(2)
14.8 Diagnostic Applications of X-Ray Imaging
276(1)
14.9 CT Images for Stereotactic Surgeries
277(1)
14.10 CT Registration for Other Image-Guided Interventions
278(1)
14.11 Complications of X-Ray Imaging
279(1)
14.12 Summary
279(4)
Problems
279(4)
Chapter 15 Magnetic Resonance Imaging
283(26)
15.1 Introduction and Overview
283(2)
15.2 Physical and Physiological Principles of MRI
285(6)
15.2.1 Resonance
288(3)
15.3 MR Imaging
291(4)
15.4 Formulation of MRI Reconstruction
295(2)
15.5 Functional MRI
297(4)
15.5.1 BOLD MRI
299(2)
15.6 Applications of MRI and fMRI
301(2)
15.6.1 fMRI for Monitoring Audio Activities of Brain
301(1)
15.6.2 fMRI for Monitoring Motoneuron Activities of Brain
302(1)
15.6.3 fMRI for Monitoring Visual Cortex Activities
303(1)
15.7 Processing and Feature Extraction of MRI
303(2)
15.7.1 Sources of Noise and Filtering Methods in MRI
304(1)
15.7.2 Feature Extraction
305(1)
15.8 Comparison of MRI with Other Imaging Modalities
305(1)
15.9 Registration with MR Images
306(1)
15.10 Summary
307(2)
Problems
307(2)
Chapter 16 Ultrasound Imaging
309(30)
16.1 Introduction and Overview
309(1)
16.2 Why Ultrasound Imaging?
309(1)
16.3 Generation and Detection of Ultrasound Waves
310(1)
16.4 Physical and Physiological Principles of Ultrasound
311(7)
16.4.1 Fundamental Ultrasound Concepts
311(2)
16.4.2 Wave Equation
313(1)
16.4.3 Attenuation
314(2)
16.4.4 Reflection
316(2)
16.5 Resolution of Ultrasound Imaging Systems
318(1)
16.6 Ultrasound Imaging Modalities
319(10)
16.6.1 Attenuation Tomography
320(4)
16.6.2 Ultrasound Time-of-Flight Tomography
324(1)
16.6.3 Reflection Tomography
325(2)
16.6.3.1 Doppler Ultrasound Imaging
327(2)
16.7 Modes of Ultrasound Image Representation
329(1)
16.8 Ultrasound Image Artifacts
330(1)
16.9 Three-Dimensional Ultrasound Image Reconstruction
330(2)
16.10 Applications of Ultrasound Imaging
332(1)
16.11 Processing and Feature Extraction of Ultrasonic Images
332(1)
16.12 Image Registration
333(1)
16.13 Comparison of CT, MRI, and Ultrasonic Images
334(1)
16.14 Bioeffects of Ultrasound
334(1)
16.15 Summary
335(4)
Problems
336(3)
Chapter 17 Positron Emission Tomography
339(16)
17.1 Introduction and Overview
339(1)
17.2 Physical and Physiological Principles of PET
339(3)
17.2.1 Production of Radionucleotides
340(1)
17.2.2 Degeneration Process
341(1)
17.3 PET Signal Acquisition
342(4)
17.3.1 Radioactive Detection in PET
343(3)
17.4 PET Image Formation
346(1)
17.5 Significance of PET
347(1)
17.6 Applications of PET
347(4)
17.6.1 Cancer Tumor Detection
347(1)
17.6.2 Functional Brain Mapping
348(1)
17.6.3 Functional Heart Imaging
349(1)
17.6.4 Anatomical Imaging
350(1)
17.7 Processing and Feature Extraction of PET Images
351(1)
17.7.1 Sources of Noise and Blurring in PET
351(1)
17.7.2 Image Registration with PET
351(1)
17.8 Comparison of CT, MRI, Ultrasonic, and PET Images
352(1)
17.9 Summary
353(2)
Problems
353(2)
Chapter 18 Other Biomedical Imaging Techniques
355(22)
18.1 Introduction and Overview
355(1)
18.2 Optical Microscopy
355(2)
18.3 Fluorescent Microscopy
357(3)
18.4 Confocal Microscopy
360(2)
18.5 Near-Field Scanning Optical Microscopy
362(2)
18.6 Electrical Impedance Imaging
364(2)
18.7 Electron Microscopy
366(3)
18.7.1 Transmission Electron Microscopy
367(1)
18.7.2 Scanning Electron Microscopy
367(2)
18.8 Infrared Imaging
369(1)
18.9 Biometrics
370(4)
18.9.1 Biometrics Methodology
371(1)
18.9.2 Biometrics Using Fingerprints
372(1)
18.9.3 Biometrics Using Retina Scans
373(1)
18.9.4 Biometrics Using Iris Scans
374(1)
18.10 Summary
374(3)
Problems
375(2)
Index 377
Kayvan Najarian, Ph.D., is Associate Professor of Computer Science, and Director of the VCU Biomedical Signal and Image Processing Engineering Center within the Department of Computer Science at Virginia Commonwealth University, in Richmond, Virginia.
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