{"product_id":"medical-image-analysis-isbn-9780470622056","title":"Medical Image Analysis","description":"\u003cb\u003eThe expanded and revised edition will split Chapter 4 to include more details and examples in FMRI, DTI, and DWI for MR image modalities. The book will also expand ultrasound imaging to 3-D dynamic contrast ultrasound imaging in a separate chapter.\u003c\/b\u003e \u003cp\u003eA new chapter on Optical Imaging Modalities elaborating microscopy, confocal microscopy, endoscopy, optical coherent tomography, fluorescence and molecular imaging will be added. Another new chapter on Simultaneous Multi-Modality Medical Imaging including CT-SPECT and CT-PET will also be added. In the image analysis part, chapters on image reconstructions and visualizations will be significantly enhanced to include, respectively, 3-D fast statistical estimation based reconstruction methods, and 3-D image fusion and visualization overlaying multi-modality imaging and information. A new chapter on Computer-Aided Diagnosis and image guided surgery, and surgical and therapeutic intervention will also be added.\u003c\/p\u003e \u003cp\u003eA companion site containing power point slides, author biography, corrections to the first edition and images from the text can be found here: \u003cb\u003ewiley.com\/public\/sci_tech_med\/medical_image\/\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSend an email to: \u003cb\u003ePressbooks@ieee.org\u003c\/b\u003e to obtain a solutions manual. Please include your affiliation in your email.\u003c\/p\u003e \u003cp\u003ePreface to the Second Edition xiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 \u003c\/b\u003e\u003cb\u003eIntroduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1. Medical Imaging: A Collaborative Paradigm 2\u003c\/p\u003e \u003cp\u003e1.2. Medical Imaging Modalities 3\u003c\/p\u003e \u003cp\u003e1.3. Medical Imaging: from Physiology to Information Processing 6\u003c\/p\u003e \u003cp\u003e1.3.1 Understanding Physiology and Imaging Medium 6\u003c\/p\u003e \u003cp\u003e1.3.2 Physics of Imaging 7\u003c\/p\u003e \u003cp\u003e1.3.3 Imaging Instrumentation 7\u003c\/p\u003e \u003cp\u003e1.3.4 Data Acquisition and Image Reconstruction 7\u003c\/p\u003e \u003cp\u003e1.3.5 Image Analysis and Applications 8\u003c\/p\u003e \u003cp\u003e1.4. General Performance Measures 8\u003c\/p\u003e \u003cp\u003e1.4.1 An Example of Performance Measure 10\u003c\/p\u003e \u003cp\u003e1.5. Biomedical Image Processing and Analysis 11\u003c\/p\u003e \u003cp\u003e1.6. Matlab Image Processing Toolbox 14\u003c\/p\u003e \u003cp\u003e1.6.1 Digital Image Representation 14\u003c\/p\u003e \u003cp\u003e1.6.2 Basic MATLAB Image Toolbox Commands 16\u003c\/p\u003e \u003cp\u003e1.7. Imagepro Interface in Matlab Environment and Image Databases 19\u003c\/p\u003e \u003cp\u003e1.7.1 Imagepro Image Processing Interface 19\u003c\/p\u003e \u003cp\u003e1.7.2 Installation Instructions 20\u003c\/p\u003e \u003cp\u003e1.8. Imagej and Other Image Processing Software Packages 20\u003c\/p\u003e \u003cp\u003e1.9. Exercises 21\u003c\/p\u003e \u003cp\u003e1.10. References 22\u003c\/p\u003e \u003cp\u003e1.11. Definitions 22\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 \u003c\/b\u003e\u003cb\u003eImage Formation23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1. Image Coordinate System 24\u003c\/p\u003e \u003cp\u003e2.1.1 2-D Image Rotation 25\u003c\/p\u003e \u003cp\u003e2.1.2 3-D Image Rotation and Translation Transformation 26\u003c\/p\u003e \u003cp\u003e2.2. Linear Systems 27\u003c\/p\u003e \u003cp\u003e2.3. Point Source and Impulse Functions 27\u003c\/p\u003e \u003cp\u003e2.4. Probability and Random Variable Functions 29\u003c\/p\u003e \u003cp\u003e2.4.1 Conditional and Joint Probability Density Functions 30\u003c\/p\u003e \u003cp\u003e2.4.2 Independent and Orthogonal Random Variables 31\u003c\/p\u003e \u003cp\u003e2.5. Image Formation 32\u003c\/p\u003e \u003cp\u003e2.5.1 PSF and Spatial Resolution 35\u003c\/p\u003e \u003cp\u003e2.5.2 Signal-to-Noise Ratio 37\u003c\/p\u003e \u003cp\u003e2.5.3 Contrast-to-Noise Ratio 39\u003c\/p\u003e \u003cp\u003e2.6. Pin-hole Imaging 39\u003c\/p\u003e \u003cp\u003e2.7. Fourier Transform 40\u003c\/p\u003e \u003cp\u003e2.7.1 Sinc Function 43\u003c\/p\u003e \u003cp\u003e2.8. Radon Transform 44\u003c\/p\u003e \u003cp\u003e2.9. Sampling 46\u003c\/p\u003e \u003cp\u003e2.10. Discrete Fourier Transform 50\u003c\/p\u003e \u003cp\u003e2.11. Wavelet Transform 52\u003c\/p\u003e \u003cp\u003e2.12. Exercises 60\u003c\/p\u003e \u003cp\u003e2.13. References 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 \u003c\/b\u003e\u003cb\u003eInteraction of Electromagnetic Radiation with Matter in Medical Imaging 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1. Electromagnetic Radiation 65\u003c\/p\u003e \u003cp\u003e3.2. Electromagnetic Radiation for Image Formation 66\u003c\/p\u003e \u003cp\u003e3.3. Radiation Interaction with Matter 67\u003c\/p\u003e \u003cp\u003e3.3.1 Coherent or Rayleigh Scattering 67\u003c\/p\u003e \u003cp\u003e3.3.2 Photoelectric Absorption 68\u003c\/p\u003e \u003cp\u003e3.3.3 Compton Scattering 69\u003c\/p\u003e \u003cp\u003e3.3.4 Pair Production 69\u003c\/p\u003e \u003cp\u003e3.4. Linear Attenuation Coefficient 70\u003c\/p\u003e \u003cp\u003e3.5. Radiation Detection 70\u003c\/p\u003e \u003cp\u003e3.5.1 Ionized Chambers and Proportional Counters 70\u003c\/p\u003e \u003cp\u003e3.5.2 Semiconductor Detectors 72\u003c\/p\u003e \u003cp\u003e3.5.3 Advantages of Semiconductor Detectors 73\u003c\/p\u003e \u003cp\u003e3.5.4 Scintillation Detectors 73\u003c\/p\u003e \u003cp\u003e3.6. Detector Subsystem Output Voltage Pulse 76\u003c\/p\u003e \u003cp\u003e3.7. Exercises 78\u003c\/p\u003e \u003cp\u003e3.8. References 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 \u003c\/b\u003e\u003cb\u003eMedical Imaging Modalities: X-Ray Imaging 79\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1. X-Ray Imaging 80\u003c\/p\u003e \u003cp\u003e4.2. X-Ray Generation 81\u003c\/p\u003e \u003cp\u003e4.3. X-Ray 2-D Projection Imaging 84\u003c\/p\u003e \u003cp\u003e4.4. X-Ray Mammography 86\u003c\/p\u003e \u003cp\u003e4.5. X-Ray CT 88\u003c\/p\u003e \u003cp\u003e4.6. Spiral X-Ray CT 92\u003c\/p\u003e \u003cp\u003e4.7. Contrast Agent, Spatial Resolution, and SNR 95\u003c\/p\u003e \u003cp\u003e4.8. Exercises 96\u003c\/p\u003e \u003cp\u003e4.9. References 97\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 \u003c\/b\u003e\u003cb\u003eMedical Imaging Modalities: Magnetic Resonance Imaging 99\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1. MRI Principles 100\u003c\/p\u003e \u003cp\u003e5.2. MR Instrumentation 110\u003c\/p\u003e \u003cp\u003e5.3. MRI Pulse Sequences 112\u003c\/p\u003e \u003cp\u003e5.3.1 Spin-Echo Imaging 114\u003c\/p\u003e \u003cp\u003e5.3.2 Inversion Recovery Imaging 118\u003c\/p\u003e \u003cp\u003e5.3.3 Echo Planar Imaging 119\u003c\/p\u003e \u003cp\u003e5.3.4 Gradient Echo Imaging 123\u003c\/p\u003e \u003cp\u003e5.4. Flow Imaging 125\u003c\/p\u003e \u003cp\u003e5.5. fMRI 129\u003c\/p\u003e \u003cp\u003e5.6. Diffusion Imaging 130\u003c\/p\u003e \u003cp\u003e5.7. Contrast, Spatial Resolution, and SNR 135\u003c\/p\u003e \u003cp\u003e5.8. Exercises 137\u003c\/p\u003e \u003cp\u003e5.9. References 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 \u003c\/b\u003e\u003cb\u003eNuclear Medicine Imaging Modalities 139\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1. Radioactivity 139\u003c\/p\u003e \u003cp\u003e6.2. SPECT 140\u003c\/p\u003e \u003cp\u003e6.2.1 Detectors and Data Acquisition System 142\u003c\/p\u003e \u003cp\u003e6.2.2 Contrast, Spatial Resolution, and Signal-to-Noise Ratio in SPECT Imaging 145\u003c\/p\u003e \u003cp\u003e6.3. PET 148\u003c\/p\u003e \u003cp\u003e6.3.1 Detectors and Data Acquisition Systems 150\u003c\/p\u003e \u003cp\u003e6.3.2 Contrast, Spatial Resolution, and SNR in PET Imaging 150\u003c\/p\u003e \u003cp\u003e6.4. Dual-Modality Spect–CT and PET–CT Scanners 151\u003c\/p\u003e \u003cp\u003e6.5. Exercises 154\u003c\/p\u003e \u003cp\u003e6.6. References 155\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 \u003c\/b\u003e\u003cb\u003eMedical Imaging Modalities: Ultrasound Imaging 157\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1. Propagation of Sound in a Medium 157\u003c\/p\u003e \u003cp\u003e7.2. Reflection and Refraction 159\u003c\/p\u003e \u003cp\u003e7.3. Transmission of Ultrasound Waves in a Multilayered Medium 160\u003c\/p\u003e \u003cp\u003e7.4. Attenuation 162\u003c\/p\u003e \u003cp\u003e7.5. Ultrasound Reflection Imaging 163\u003c\/p\u003e \u003cp\u003e7.6. Ultrasound Imaging Instrumentation 164\u003c\/p\u003e \u003cp\u003e7.7. Imaging with Ultrasound: A-Mode 166\u003c\/p\u003e \u003cp\u003e7.8. Imaging with Ultrasound: M-Mode 167\u003c\/p\u003e \u003cp\u003e7.9. Imaging with Ultrasound: B-Mode 168\u003c\/p\u003e \u003cp\u003e7.10. Doppler Ultrasound Imaging 169\u003c\/p\u003e \u003cp\u003e7.11. Contrast, Spatial Resolution, and SNR 170\u003c\/p\u003e \u003cp\u003e7.12. Exercises 171\u003c\/p\u003e \u003cp\u003e7.13. References 172\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 \u003c\/b\u003e\u003cb\u003eImage Reconstruction 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1. Radon Transform and Image Reconstruction 174\u003c\/p\u003e \u003cp\u003e8.1.1 The Central Slice Theorem 174\u003c\/p\u003e \u003cp\u003e8.1.2 Inverse Radon Transform 176\u003c\/p\u003e \u003cp\u003e8.1.3 Backprojection Method 176\u003c\/p\u003e \u003cp\u003e8.2. Iterative Algebraic Reconstruction Methods 180\u003c\/p\u003e \u003cp\u003e8.3. Estimation Methods 182\u003c\/p\u003e \u003cp\u003e8.4. Fourier Reconstruction Methods 185\u003c\/p\u003e \u003cp\u003e8.5. Image Reconstruction in Medical Imaging Modalities 186\u003c\/p\u003e \u003cp\u003e8.5.1 Image Reconstruction in X-Ray CT 186\u003c\/p\u003e \u003cp\u003e8.5.2 Image Reconstruction in Nuclear Emission Computed Tomography: SPECT and PET 188\u003c\/p\u003e \u003cp\u003e8.5.2.1 A General Approach to ML–EM Algorithms 189\u003c\/p\u003e \u003cp\u003e8.5.2.2 A Multigrid EM Algorithm 190\u003c\/p\u003e \u003cp\u003e8.5.3 Image Reconstruction in Magnetic Resonance Imaging 192\u003c\/p\u003e \u003cp\u003e8.5.4 Image Reconstruction in Ultrasound Imaging 193\u003c\/p\u003e \u003cp\u003e8.6. Exercises 194\u003c\/p\u003e \u003cp\u003e8.7. References 195\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 \u003c\/b\u003e\u003cb\u003eImage Processing and Enhancement 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1. Spatial Domain Methods 200\u003c\/p\u003e \u003cp\u003e9.1.1 Histogram Transformation and Equalization 201\u003c\/p\u003e \u003cp\u003e9.1.2 Histogram Modification 203\u003c\/p\u003e \u003cp\u003e9.1.3 Image Averaging 204\u003c\/p\u003e \u003cp\u003e9.1.4 Image Subtraction 204\u003c\/p\u003e \u003cp\u003e9.1.5 Neighborhood Operations 205\u003c\/p\u003e \u003cp\u003e9.1.5.1 Median Filter 207\u003c\/p\u003e \u003cp\u003e9.1.5.2 Adaptive Arithmetic Mean Filter 207\u003c\/p\u003e \u003cp\u003e9.1.5.3 Image Sharpening and Edge Enhancement 208\u003c\/p\u003e \u003cp\u003e9.1.5.4 Feature Enhancement Using Adaptive Neighborhood Processing 209\u003c\/p\u003e \u003cp\u003e9.2. Frequency Domain Filtering 212\u003c\/p\u003e \u003cp\u003e9.2.1 Wiener Filtering 213\u003c\/p\u003e \u003cp\u003e9.2.2 Constrained Least Square Filtering 214\u003c\/p\u003e \u003cp\u003e9.2.3 Low-Pass Filtering 215\u003c\/p\u003e \u003cp\u003e9.2.4 High-Pass Filtering 217\u003c\/p\u003e \u003cp\u003e9.2.5 Homomorphic Filtering 217\u003c\/p\u003e \u003cp\u003e9.3. Wavelet Transform for Image Processing 220\u003c\/p\u003e \u003cp\u003e9.3.1 Image Smoothing and Enhancement Using Wavelet Transform 223\u003c\/p\u003e \u003cp\u003e9.4. Exercises 226\u003c\/p\u003e \u003cp\u003e9.5. References 228\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 \u003c\/b\u003e\u003cb\u003eImage Segmentation 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1. Edge-Based Image Segmentation 229\u003c\/p\u003e \u003cp\u003e10.1.1 Edge Detection Operations 230\u003c\/p\u003e \u003cp\u003e10.1.2 Boundary Tracking 231\u003c\/p\u003e \u003cp\u003e10.1.3 Hough Transform 233\u003c\/p\u003e \u003cp\u003e10.2. Pixel-Based Direct Classification Methods 235\u003c\/p\u003e \u003cp\u003e10.2.1 Optimal Global Thresholding 237\u003c\/p\u003e \u003cp\u003e10.2.2 Pixel Classification Through Clustering 239\u003c\/p\u003e \u003cp\u003e10.2.2.1 Data Clustering 239\u003c\/p\u003e \u003cp\u003e10.2.2.2 k-Means Clustering 241\u003c\/p\u003e \u003cp\u003e10.2.2.3 Fuzzy c-Means Clustering 242\u003c\/p\u003e \u003cp\u003e10.2.2.4 An Adaptive FCM Algorithm 244\u003c\/p\u003e \u003cp\u003e10.3. Region-Based Segmentation 245\u003c\/p\u003e \u003cp\u003e10.3.1 Region-Growing 245\u003c\/p\u003e \u003cp\u003e10.3.2 Region-Splitting 247\u003c\/p\u003e \u003cp\u003e10.4. Advanced Segmentation Methods 248\u003c\/p\u003e \u003cp\u003e10.4.1 Estimation-Model Based Adaptive Segmentation 249\u003c\/p\u003e \u003cp\u003e10.4.2 Image Segmentation Using Neural Networks 254\u003c\/p\u003e \u003cp\u003e10.4.2.1 Backpropagation Neural Network for Classification 255\u003c\/p\u003e \u003cp\u003e10.4.2.2 The RBF Network 258\u003c\/p\u003e \u003cp\u003e10.4.2.3 Segmentation of Arterial Structure in Digital Subtraction Angiograms 259\u003c\/p\u003e \u003cp\u003e10.5. Exercises 261\u003c\/p\u003e \u003cp\u003e10.6. References 262\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 \u003c\/b\u003e\u003cb\u003eImage Representation, Analysis, and Classification 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1. Feature Extraction and Representation 268\u003c\/p\u003e \u003cp\u003e11.1.1 Statistical Pixel-Level Features 268\u003c\/p\u003e \u003cp\u003e11.1.2 Shape Features 270\u003c\/p\u003e \u003cp\u003e11.1.2.1 Boundary Encoding: Chain Code 271\u003c\/p\u003e \u003cp\u003e11.1.2.2 Boundary Encoding: Fourier Descriptor 273\u003c\/p\u003e \u003cp\u003e11.1.2.3 Moments for Shape Description 273\u003c\/p\u003e \u003cp\u003e11.1.2.4 Morphological Processing for Shape Description 274\u003c\/p\u003e \u003cp\u003e11.1.3 Texture Features 280\u003c\/p\u003e \u003cp\u003e11.1.4 Relational Features 282\u003c\/p\u003e \u003cp\u003e11.2. Feature Selection for Classification 283\u003c\/p\u003e \u003cp\u003e11.2.1 Linear Discriminant Analysis 285\u003c\/p\u003e \u003cp\u003e11.2.2 PCA 288\u003c\/p\u003e \u003cp\u003e11.2.3 GA-Based Optimization 289\u003c\/p\u003e \u003cp\u003e11.3. Feature and Image Classification 292\u003c\/p\u003e \u003cp\u003e11.3.1 Statistical Classification Methods 292\u003c\/p\u003e \u003cp\u003e11.3.1.1 Nearest Neighbor Classifier 293\u003c\/p\u003e \u003cp\u003e11.3.1.2 Bayesian Classifier 293\u003c\/p\u003e \u003cp\u003e11.3.2 Rule-Based Systems 294\u003c\/p\u003e \u003cp\u003e11.3.3 Neural Network Classifiers 296\u003c\/p\u003e \u003cp\u003e11.3.3.1 Neuro-Fuzzy Pattern Classification 296\u003c\/p\u003e \u003cp\u003e11.3.4 Support Vector Machine for Classification 302\u003c\/p\u003e \u003cp\u003e11.4. Image Analysis and Classification Example: “Difficult-To-Diagnose” Mammographic Microcalcifications 303\u003c\/p\u003e \u003cp\u003e11.5. Exercises 306\u003c\/p\u003e \u003cp\u003e11.6. References 307\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 \u003c\/b\u003e\u003cb\u003eImage Registration 311\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e12.1. Rigid-Body Transformation 314\u003c\/p\u003e \u003cp\u003e12.1.1 Affine Transformation 316\u003c\/p\u003e \u003cp\u003e12.2. Principal Axes Registration 316\u003c\/p\u003e \u003cp\u003e12.3. Iterative Principal Axes Registration 319\u003c\/p\u003e \u003cp\u003e12.4. Image Landmarks and Features-Based Registration 323\u003c\/p\u003e \u003cp\u003e12.4.1 Similarity Transformation for Point-Based Registration 323\u003c\/p\u003e \u003cp\u003e12.4.2 Weighted Features-Based Registration 324\u003c\/p\u003e \u003cp\u003e12.5. Elastic Deformation-Based Registration 325\u003c\/p\u003e \u003cp\u003e12.6. Exercises 330\u003c\/p\u003e \u003cp\u003e12.7. References 331\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 \u003c\/b\u003e\u003cb\u003eImage Visualization 335\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e13.1. Feature-Enhanced 2-D Image Display Methods 336\u003c\/p\u003e \u003cp\u003e13.2. Stereo Vision and Semi-3-D Display Methods 336\u003c\/p\u003e \u003cp\u003e13.3. Surface- and Volume-Based 3-D Display Methods 338\u003c\/p\u003e \u003cp\u003e13.3.1 Surface Visualization 339\u003c\/p\u003e \u003cp\u003e13.3.2 Volume Visualization 344\u003c\/p\u003e \u003cp\u003e13.4. VR-Based Interactive Visualization 347\u003c\/p\u003e \u003cp\u003e13.4.1 Virtual Endoscopy 349\u003c\/p\u003e \u003cp\u003e13.5. Exercises 349\u003c\/p\u003e \u003cp\u003e13.6. References 350\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 \u003c\/b\u003e\u003cb\u003eCurrent and Future Trends in Medical Imaging and Image Analysis 353\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e14.1. Multiparameter Medical Imaging and Analysis 353\u003c\/p\u003e \u003cp\u003e14.2. Targeted Imaging 357\u003c\/p\u003e \u003cp\u003e14.3. Optical Imaging and Other Emerging Modalities 357\u003c\/p\u003e \u003cp\u003e14.3.1 Optical Microscopy 358\u003c\/p\u003e \u003cp\u003e14.3.2 Optical Endoscopy 360\u003c\/p\u003e \u003cp\u003e14.3.3 Optical Coherence Tomography 360\u003c\/p\u003e \u003cp\u003e14.3.4 Diffuse Reflectance and Transillumination Imaging 362\u003c\/p\u003e \u003cp\u003e14.3.5 Photoacoustic Imaging: An Emerging Technology 363\u003c\/p\u003e \u003cp\u003e14.4. Model-Based and Multiscale Analysis 364\u003c\/p\u003e \u003cp\u003e14.5. References 366\u003c\/p\u003e \u003cp\u003eIndex 503\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eATAM P. DHAWAN, P\u003csmal\u003eHD,\u003c\/smal\u003e\u003c\/b\u003e 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.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003e\u003ci\u003eNow updated—the most comprehensive reference of medical imaging modalities and image analysis techniques\u003c\/i\u003e\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe last two decades have witnessed revolutionary advances in medical imaging and computerized medical image processing. With the advent and enhancement of numerous sophisticated medical imaging modalities, intelligent processing of multi-dimensional images has become critical in radiological and diagnostic applications.\u003c\/p\u003e \u003cp\u003eThis benchmark text takes a unique, all-inclusive approach to the topic—one that weaves together medical physics, medical imaging instrumentation, and advanced image analysis methods. This \u003ci\u003eSecond Edition\u003c\/i\u003e is completely revised and expanded to provide a broader foundation, helping engineers, medical professionals, and students alike understand medical imaging principles, perform intelligent image interpretation, and navigate the intricacies of instrumentation, data collection, image reconstruction, and computerized image analysis for radiological computer-aided evaluation and diagnosis. New chapters cover:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eMore in-depth description of recent developments in medical imaging instrumentation, including spiral CT, diagnostic ultrasound, functional MRI, and Diffusion Tension Imaging\u003c\/li\u003e \u003cli\u003eSimultaneous multi-modality medical imaging, including CT-SPECT and CT-PET\u003c\/li\u003e \u003cli\u003eAdvanced medical image analysis and classification methods for computer-aided diagnosis, and therapeutic intervention\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThis updated edition presents individual chapters focused on x-ray, MRI, nuclear medicine, and ultrasound imaging modalities with additional details and recent advances. In addition, chapters on image reconstructions and visualizations have been significantly enhanced to include, respectively, 3-D statistical estimation–based reconstruction methods, feature classification and multi-modality image visualization. Examples with clinical images for medical image analysis and computer-aided diagnosis are provided throughout, as well as skill-building MATLAB® exercises.\u003c\/p\u003e \u003cp\u003eAn ideal learning tool, this state-of-the-art resource can be used for one- or two-semester based senior undergraduate and\/or graduate-level courses. Students in medical imaging and image processing, electrical and computer engineering, computer science, and biomedical engineering as well as physicians, medical physicists, and researchers will gain the knowledge to master the complexities of today's radiological and diagnostic applications.\u003c\/p\u003e","brand":"Wiley-IEEE Press","offers":[{"title":"Default Title","offer_id":47989601468645,"sku":"NP9780470622056","price":164.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470622056.jpg?v=1761784765","url":"https:\/\/k12savings.com\/products\/medical-image-analysis-isbn-9780470622056","provider":"K12savings","version":"1.0","type":"link"}