Medical Image Analysis: (31 IEEE Press Series on Biomedical Engineering)

Table of 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. Definitions 22 Chapter 2 Image Formation23 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 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 Coefficient 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 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. Reflection and Refraction 159 7.3. Transmission of Ultrasound Waves in a Multilayered Medium 160 7.4. Attenuation 162 7.5. Ultrasound Reflection 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 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 Modification 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 Classification Methods 235 10.2.1 Optimal Global Thresholding 237 10.2.2 Pixel Classification 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 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 Classification 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 Classification 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 Classification 292 11.3.1 Statistical Classification Methods 292 11.3.1.1 Nearest Neighbor Classifier 293 11.3.1.2 Bayesian Classifier 293 11.3.2 Rule-Based Systems 294 11.3.3 Neural Network Classifiers 296 11.3.3.1 Neuro-Fuzzy Pattern Classification 296 11.3.4 Support Vector Machine for Classification 302 11.4. Image Analysis and Classification Example: “Difficult-To-Diagnose” Mammographic Microcalcifications 303 11.5. Exercises 306 11.6. References 307 Chapter 12 Image Registration 311 12.1. Rigid-Body Transformation 314 12.1.1 Affine 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 Chapter 13 Image Visualization 335 13.1. Feature-Enhanced 2-D Image Display Methods 336 13.2. Stereo Vision and Semi-3-D Display Methods 336 13.3. Surface- and Volume-Based 3-D Display Methods 338 13.3.1 Surface Visualization 339 13.3.2 Volume Visualization 344 13.4. VR-Based Interactive Visualization 347 13.4.1 Virtual Endoscopy 349 13.5. Exercises 349 13.6. References 350 Chapter 14 Current and Future Trends in Medical Imaging and Image Analysis 353 14.1. Multiparameter Medical Imaging and Analysis 353 14.2. Targeted Imaging 357 14.3. Optical Imaging and Other Emerging Modalities 357 14.3.1 Optical Microscopy 358 14.3.2 Optical Endoscopy 360 14.3.3 Optical Coherence Tomography 360 14.3.4 Diffuse Reflectance and Transillumination Imaging 362 14.3.5 Photoacoustic Imaging: An Emerging Technology 363 14.4. Model-Based and Multiscale Analysis 364 14.5. References 366 Index 503

About the Author :
ATAM P. DHAWAN, PHD, is Distinguished Professor in the Electrical and Computer Engineering Department at New Jersey Institute of Technology. He teaches courses in biomedical engineering and has supervised approximately fifty graduate students, including twenty-one PhD students. Dr. Dhawan is a Fellow of the IEEE and the recipient of numerous national and international awards. He has published more than 200 research articles in refereed journals, conference proceedings, and edited books. Dr. Dhawan has chaired numerous study sections and review panels for the National Institutes of Health in biomedical computing and medical imaging and health informatics. His current research interests are medical imaging, multi-modality medical image analysis, multi-grid image reconstruction, wavelets, genetic algorithms, neural networks, adaptive learning, and pattern recognition.