Medical Image Analysis, Second Edition

388 Pages·2011·14.668 MB·English

by  Atam P. Dhawan(auth.)

MEDICAL IMAGE
ANALYSIS
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IEEE Press
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SECOND EDITION
MEDICAL IMAGE
ANALYSIS
ATAM P. DHAWAN
IEEE Engineering in Medicine and Biology Society, Sponsor
IEEE Press Series in Biomedical Engineering
Metin Akay, Series Editor
IEEE PRESS
A JOHN WILEY & SONS, INC., PUBLICATION
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Copyright © 2011 by the Institute of Electrical and Electronics Engineers, Inc.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey
Published simultaneously in Canada
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Library of Congress Cataloging-in-Publication Data is available.
ISBN 978-0-470-622056
Printed in Singapore
oBook: 978-0-470-91854-8
eBook: 978-0-470-91853-1
ePub: 978-0-470-92289-7
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To my father
Chandar Bhan Dhawan
It is a devotion!
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CONTENTS
PREFACE TO THE SECOND EDITION xiii
CHAPTER 1 INTRODUCTION 1
1.1. Medical Imaging: A Collaborative Paradigm 2
1.2. Medical Imaging Modalities 3
1.3. Medical Imaging: from Physiology to Information Processing 6
1.3.1 Understanding Physiology and Imaging Medium 6
1.3.2 Physics of Imaging 7
1.3.3 Imaging Instrumentation 7
1.3.4 Data Acquisition and Image Reconstruction 7
1.3.5 Image Analysis and Applications 8
1.4. General Performance Measures 8
1.4.1 An Example of Performance Measure 10
1.5. Biomedical Image Processing and Analysis 11
1.6. Matlab Image Processing Toolbox 14
1.6.1 Digital Image Representation 14
1.6.2 Basic MATLAB Image Toolbox Commands 16
1.7. Imagepro Interface in Matlab Environment and Image Databases 19
1.7.1 Imagepro Image Processing Interface 19
1.7.2 Installation Instructions 20
1.8. Imagej and Other Image Processing Software Packages 20
1.9. Exercises 21
1.10. References 22
1.11. Defi nitions 22
CHAPTER 2 IMAGE FORMATION 23
2.1. Image Coordinate System 24
2.1.1 2-D Image Rotation 25
2.1.2 3-D Image Rotation and Translation Transformation 26
2.2. Linear Systems 27
2.3. Point Source and Impulse Functions 27
2.4. Probability and Random Variable Functions 29
2.4.1 Conditional and Joint Probability Density Functions 30
2.4.2 Independent and Orthogonal Random Variables 31
2.5. Image Formation 32
2.5.1 PSF and Spatial Resolution 35
2.5.2 Signal-to-Noise Ratio 37
2.5.3 Contrast-to-Noise Ratio 39
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2.6. Pin-hole Imaging 39
2.7. Fourier Transform 40
2.7.1 Sinc Function 43
2.8. Radon Transform 44
2.9. Sampling 46
2.10. Discrete Fourier Transform 50
2.11. Wavelet Transform 52
2.12. Exercises 60
2.13. References 62
CHAPTER 3 INTERACTION OF ELECTROMAGNETIC RADIATION
WITH MATTER IN MEDICAL IMAGING 65
3.1. Electromagnetic Radiation 65
3.2. Electromagnetic Radiation for Image Formation 66
3.3. Radiation Interaction with Matter 67
3.3.1 Coherent or Rayleigh Scattering 67
3.3.2 Photoelectric Absorption 68
3.3.3 Compton Scattering 69
3.3.4 Pair Production 69
3.4. Linear Attenuation Coeffi cient 70
3.5. Radiation Detection 70
3.5.1 Ionized Chambers and Proportional Counters 70
3.5.2 Semiconductor Detectors 72
3.5.3 Advantages of Semiconductor Detectors 73
3.5.4 Scintillation Detectors 73
3.6. Detector Subsystem Output Voltage Pulse 76
3.7. Exercises 78
3.8. References 78
CHAPTER 4 MEDICAL IMAGING MODALITIES: X-RAY IMAGING 79
4.1. X-Ray Imaging 80
4.2. X-Ray Generation 81
4.3. X-Ray 2-D Projection Imaging 84
4.4. X-Ray Mammography 86
4.5. X-Ray CT 88
4.6. Spiral X-Ray CT 92
4.7. Contrast Agent, Spatial Resolution, and SNR 95
4.8. Exercises 96
4.9. References 97
CHAPTER 5 MEDICAL IMAGING MODALITIES: MAGNETIC
RESONANCE IMAGING 99
5.1. MRI Principles 100
5.2. MR Instrumentation 110
5.3. MRI Pulse Sequences 112
5.3.1 Spin-Echo Imaging 114
5.3.2 Inversion Recovery Imaging 118
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CONTENTS ix
5.3.3 Echo Planar Imaging 119
5.3.4 Gradient Echo Imaging 123
5.4. Flow Imaging 125
5.5. fMRI 129
5.6. Diffusion Imaging 130
5.7. Contrast, Spatial Resolution, and SNR 135
5.8. Exercises 137
5.9. References 138
CHAPTER 6 NUCLEAR MEDICINE IMAGING MODALITIES 139
6.1. Radioactivity 139
6.2. SPECT 140
6.2.1 Detectors and Data Acquisition System 142
6.2.2 Contrast, Spatial Resolution, and Signal-to-Noise Ratio
in SPECT Imaging 145
6.3. PET 148
6.3.1 Detectors and Data Acquisition Systems 150
6.3.2 Contrast, Spatial Resolution, and SNR in PET Imaging 150
6.4. Dual-Modality Spect–CT and PET–CT Scanners 151
6.5. Exercises 154
6.6. References 155
CHAPTER 7 MEDICAL IMAGING MODALITIES: ULTRASOUND IMAGING 157
7.1. Propagation of Sound in a Medium 157
7.2. Refl ection and Refraction 159
7.3. Transmission of Ultrasound Waves in a Multilayered Medium 160
7.4. Attenuation 162
7.5. Ultrasound Refl ection Imaging 163
7.6. Ultrasound Imaging Instrumentation 164
7.7. Imaging with Ultrasound: A-Mode 166
7.8. Imaging with Ultrasound: M-Mode 167
7.9. Imaging with Ultrasound: B-Mode 168
7.10. Doppler Ultrasound Imaging 169
7.11. Contrast, Spatial Resolution, and SNR 170
7.12. Exercises 171
7.13. References 172
CHAPTER 8 IMAGE RECONSTRUCTION 173
8.1. Radon Transform and Image Reconstruction 174
8.1.1 The Central Slice Theorem 174
8.1.2 Inverse Radon Transform 176
8.1.3 Backprojection Method 176
8.2. Iterative Algebraic Reconstruction Methods 180
8.3. Estimation Methods 182
8.4. Fourier Reconstruction Methods 185
8.5. Image Reconstruction in Medical Imaging Modalities 186
8.5.1 Image Reconstruction in X-Ray CT 186
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8.5.2 Image Reconstruction in Nuclear Emission Computed
Tomography: SPECT and PET 188
8.5.2.1 A General Approach to ML–EM Algorithms 189
8.5.2.2 A Multigrid EM Algorithm 190
8.5.3 Image Reconstruction in Magnetic Resonance Imaging 192
8.5.4 Image Reconstruction in Ultrasound Imaging 193
8.6. Exercises 194
8.7. References 195
CHAPTER 9 IMAGE PROCESSING AND ENHANCEMENT 199
9.1. Spatial Domain Methods 200
9.1.1 Histogram Transformation and Equalization 201
9.1.2 Histogram Modifi cation 203
9.1.3 Image Averaging 204
9.1.4 Image Subtraction 204
9.1.5 Neighborhood Operations 205
9.1.5.1 Median Filter 207
9.1.5.2 Adaptive Arithmetic Mean Filter 207
9.1.5.3 Image Sharpening and Edge Enhancement 208
9.1.5.4 Feature Enhancement Using Adaptive Neighborhood
Processing 209
9.2. Frequency Domain Filtering 212
9.2.1 Wiener Filtering 213
9.2.2 Constrained Least Square Filtering 214
9.2.3 Low-Pass Filtering 215
9.2.4 High-Pass Filtering 217
9.2.5 Homomorphic Filtering 217
9.3. Wavelet Transform for Image Processing 220
9.3.1 Image Smoothing and Enhancement Using Wavelet
Transform 223
9.4. Exercises 226
9.5. References 228
CHAPTER 10 IMAGE SEGMENTATION 229
10.1. Edge-Based Image Segmentation 229
10.1.1 Edge Detection Operations 230
10.1.2 Boundary Tracking 231
10.1.3 Hough Transform 233
10.2. Pixel-Based Direct Classifi cation Methods 235
10.2.1 Optimal Global Thresholding 237
10.2.2 Pixel Classifi cation Through Clustering 239
10.2.2.1 Data Clustering 239
10.2.2.2 k-Means Clustering 241
10.2.2.3 Fuzzy c-Means Clustering 242
10.2.2.4 An Adaptive FCM Algorithm 244
10.3. Region-Based Segmentation 245
10.3.1 Region-Growing 245
10.3.2 Region-Splitting 247
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CONTENTS xi
10.4. Advanced Segmentation Methods 248
10.4.1 Estimation-Model Based Adaptive Segmentation 249
10.4.2 Image Segmentation Using Neural Networks 254
10.4.2.1 Backpropagation Neural Network for Classifi cation 255
10.4.2.2 The RBF Network 258
10.4.2.3 Segmentation of Arterial Structure in Digital Subtraction
Angiograms 259
10.5. Exercises 261
10.6. References 262
CHAPTER 11 IMAGE REPRESENTATION, ANALYSIS, AND CLASSIFICATION 265
11.1. Feature Extraction and Representation 268
11.1.1 Statistical Pixel-Level Features 268
11.1.2 Shape Features 270
11.1.2.1 Boundary Encoding: Chain Code 271
11.1.2.2 Boundary Encoding: Fourier Descriptor 273
11.1.2.3 Moments for Shape Description 273
11.1.2.4 Morphological Processing for Shape Description 274
11.1.3 Texture Features 280
11.1.4 Relational Features 282
11.2. Feature Selection for Classifi cation 283
11.2.1 Linear Discriminant Analysis 285
11.2.2 PCA 288
11.2.3 GA-Based Optimization 289
11.3. Feature and Image Classifi cation 292
11.3.1 Statistical Classifi cation Methods 292
11.3.1.1 Nearest Neighbor Classifi er 293
11.3.1.2 Bayesian Classifi er 293
11.3.2 Rule-Based Systems 294
11.3.3 Neural Network Classifi ers 296
11.3.3.1 Neuro-Fuzzy Pattern Classifi cation 296
11.3.4 Support Vector Machine for Classifi cation 302
11.4. Image Analysis and Classifi cation Example: “Diffi cult-To-Diagnose”
Mammographic Microcalcifi cations 303
11.5. Exercises 306
11.6. References 307
CHAPTER 12 IMAGE REGISTRATION 311
12.1. Rigid-Body Transformation 314
12.1.1 Affi ne Transformation 316
12.2. Principal Axes Registration 316
12.3. Iterative Principal Axes Registration 319
12.4. Image Landmarks and Features-Based Registration 323
12.4.1 Similarity Transformation for Point-Based Registration 323
12.4.2 Weighted Features-Based Registration 324
12.5. Elastic Deformation-Based Registration 325
12.6. Exercises 330
12.7. References 331
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