The second edition is extensively revised and updated throughout, reflecting new technology and research, and includes new chapters on: higher order statistics for tissue segmentation, tumor growth modeling in oncological image analysis, analysis of cell nuclear features in fluorescence microscopy images, imaging and communication in medical and public health informatics, and dynamic mammogram retrieval from web-based image libraries.
For those looking to explore advanced concepts and access essential information, this second edition of Handbook of Medical Image Processing and Analysis is an invaluable resource. It remains the most complete single volume reference for biomedical engineers, researchers, professionals and those working in medical imaging and medical image processing.
Dr. Isaac N. Bankman is the supervisor of a group that specializes on imaging, laser and sensor systems, modeling, algorithms and testing at the Johns Hopkins University Applied Physics Laboratory. He received his BSc degree in Electrical Engineering from Bogazici University, Turkey, in 1977, the MSc degree in Electronics from University of Wales, Britain, in 1979, and a PhD in Biomedical Engineering from the Israel Institute of Technology, Israel, in 1985. He is a member of SPIE.
* Includes contributions from internationally renowned authors from leading institutions
* NEW! 35 of 56 chapters have been revised and updated. Additionally, five new chapters have been added on important topics incluling Nonlinear 3D Boundary Detection, Adaptive Algorithms for Cancer Cytological Diagnosis, Dynamic Mammogram Retrieval from Web-Based Image Libraries, Imaging and Communication in Health Informatics and Tumor Growth Modeling in Oncological Image Analysis.
* Provides a complete collection of algorithms in computer processing of medical images
* Contains over 60 pages of stunning, four-color images
The Handbook of Medical Image Processing and Analysis is a comprehensive compilation of concepts and techniques used for processing and analyzing medical images after they have been generated or digitized. The Handbook is organized into six sections that relate to the main functions: enhancement, segmentation, quantification, registration, visualization, and compression, storage and communication.The second edition is extensively revised and updated throughout, reflecting new technology and research, and includes new chapters on: higher order statistics for tissue segmentation; tumor growth modeling in oncological image analysis; analysis of cell nuclear features in fluorescence microscopy images; imaging and communication in medical and public health informatics; and dynamic mammogram retrieval from web-based image libraries.For those looking to explore advanced concepts and access essential information, this second edition of Handbook of Medical Image Processing and Analysis is an invaluable resource. It remains the most complete single volume reference for biomedical engineers, researchers, professionals and those working in medical imaging and medical image processing.Dr. Isaac N. Bankman is the supervisor of a group that specializes on imaging, laser and sensor systems, modeling, algorithms and testing at the Johns Hopkins University Applied Physics Laboratory. He received his BSc degree in Electrical Engineering from Bogazici University, Turkey, in 1977, the MSc degree in Electronics from University of Wales, Britain, in 1979, and a PhD in Biomedical Engineering from the Israel Institute of Technology, Israel, in 1985. He is a member of SPIE. Includes contributions from internationally renowned authors from leading institutions NEW! 35 of 56 chapters have been revised and updated. Additionally, five new chapters have been added on important topics incluling Nonlinear 3D Boundary Detection, Adaptive Algorithms for Cancer Cytological Diagnosis, Dynamic Mammogram Retrieval from Web-Based Image Libraries, Imaging and Communication in Health Informatics and Tumor Growth Modeling in Oncological Image Analysis. Provides a complete collection of algorithms in computer processing of medical images Contains over 60 pages of stunning, four-color images
Front Cover 1
Handbook of Medical Image Processing and Analysis 4
Copyright Page 5
Contents 6
Foreword 17
Contributors 18
Preface 22
Acknowledgments 25
Part I Enhancement 26
Chapter 1 Fundamental Enhancement Techniques 28
1.1 Introduction 28
1.2 Preliminaries and Definitions 28
1.3 Pixel Operations 29
1.4 Local Operators 33
1.5 Operations with Multiple Images 39
1.6 Frequency Domain Techniques 40
1.7 Concluding Remarks 42
1.8 References 43
Chapter 2 Adaptive Image Filtering 44
2.1 Introduction 44
2.2 Multidimensional Spatial Frequencies and Filtering 44
2.3 Random Fields and Wiener Filtering 47
2.4 Adaptive Wiener Filters 50
2.5 Anisotropic Adaptive Filtering 51
2.6 References 57
Chapter 3 Enhancement by Multiscale Nonlinear Operators 60
3.1 Introduction 60
3.2 One-Dimensional Discrete Dyadic Wavelet Transform 61
3.3 Linear Enhancement and Unsharp Masking 64
3.4 Nonlinear Enhancement by Functional Mapping 65
3.5 A Method for Combined Denoising and Enhancement 68
3.6 Two-Dimensional Extension 72
3.7 Experimental Results and Comparison 72
3.8 Conclusion 76
3.9 References 80
Chapter 4 Medical Image Enhancement Using Fourier Descriptors and Hybrid Filters 84
4.1 Introduction 84
4.2 Design of the Hybrid Filter 85
4.3 Experimental Results 91
4.4 Discussion and Conclusion 93
4.5 References 93
Part II Segmentation 96
Chapter 5 Overview and Fundamentals of Medical Image Segmentation 98
5.1 Introduction 98
5.2 Thresholding 99
5.3 Region Growing 102
5.4 Watershed Algorithm 103
5.5 Edge-Based Segmentation Techniques 104
5.6 Multispectral Techniques 106
5.7 Other Techniques 108
5.8 Concluding Remarks 109
5.9 References 109
Chapter 6 Image Segmentation by Fuzzy Clustering: Methods and Issues 116
6.1 Introduction 116
6.2 The Quantitative Basis of Fuzzy Image Segmentation 116
6.3 Qualitative Discussion of a Few Fuzzy Image Segmentation Methods 127
6.4 Conclusions and Discussion 132
6.5 References 134
Chapter 7 Segmentation with Neural Networks 138
7.1 Introduction 138
7.2 Structure and Function of the GRBF Network 139
7.3 Training Procedure 140
7.4 Application to Medical Image Segmentation 145
7.5 Image Data 145
7.6 Preprocessing 146
7.7 Vector Quantization 149
7.8 Classification 150
7.9 Results 151
7.10 Discussion 154
7.11 Topical Applications, Conceptual Extensions, and Outlook 157
7.12 Conclusion and Outlook 163
7.13 References 164
Chapter 8 Deformable Models 170
8.1 Introduction 170
8.2 Mathematical Foundations of Deformable Models 171
8.3 Medical Image Analysis with Deformable Models 173
8.4 Discussion 183
8.5 Conclusion 185
8.6 References 185
Chapter 9 Shape Information in Deformable Models 192
9.1 Background 192
9.2 Global Shape Constraints 194
9.3 Level Set Methods Incorporating Generic Constraints 199
9.4 Conclusions 203
9.5 References 203
Chapter 10 Gradient Vector Flow Deformable Models 206
10.1 Introduction 206
10.2 Background 207
10.3 GVF Deformable Contours 209
10.4 Experiments 211
10.5 3D GVF Deformable Models and Results 213
10.6 Discussion 214
10.7 Conclusions 216
10.8 References 217
Chapter 11 Fully Automated Hybrid Segmentation of the Brain 220
11.1 Introduction 220
11.2 Brain Segmentation Method 221
11.3 Other Brain Segmentation Techniques 228
11.4 Summary 230
11.5 References 230
Chapter 12 Unsupervised Tissue Classification 234
12.1 Introduction 234
12.2 Background 234
12.3 Methods 235
12.4 Results 239
12.5 Conclusions 243
12.6 References 246
Chapter 13 Partial Volume Segmentation and Boundary Distance Estimation with Voxel Histograms 248
13.1 Introduction 249
13.2 Overview 252
13.3 Normalized Histograms 254
13.4 Histogram Basis Functions for Pure Materials and Mixtures 255
13.5 Estimating Histogram Basis Function Parameters 257
13.6 Classification 258
13.7 Results 259
13.8 Derivation of Histogram Basis Functions 261
13.9 Derivation of Classification Parameter Estimation 262
13.10 Discussion 264
13.11 Conclusions 266
13.12 References 267
Chapter 14 High Order Statistics for Tissue Segmentation 270
14.1 Introduction 270
14.2 Requirements for Using 3rd and 4th Order Statistics 272
14.3 3D Non-linear Edge Detectors 275
14.4 Experiments with Real Data 275
14.5 Discussion and Conclusions 278
14.6 References 281
Part III Quantification 284
Chapter 15 Two-Dimensional Shape and Texture Quantification 286
15.1 Shape Quantification 286
15.2 Texture Quantification 295
15.3 References 300
Chapter 16 Texture Analysis in 3D for Tissue Characterization 304
16.1 Introduction 304
16.2 Issues Related to 3D Texture Estimation and Representation 304
16.3 3D Texture Representation 305
16.4 Feature Extraction 308
16.5 Simulated Data Studies 308
16.6 Applications to Real Data 310
16.7 Conclusions 315
16.8 References 317
Chapter 17 Computational Neuroanatomy Using Shape Transformations 318
17.1 Quantifying Anatomy via Shape Transformations 319
17.2 The Shape Transformation 320
17.3 Measurements Based on the Shape Transformation 322
17.4 Spatial Normalization of Image Data 324
17.5 Conclusion 327
17.6 References 328
Chapter 18 Tumor Growth Modeling in Oncological Image Analysis 330
18.1 Introduction 330
18.2 Mathematical Models 331
18.3 Image-Guided Tools for Therapy Planning 334
18.4 Applications to Registration and Segmentation 336
18.5 Perspectives and Challenges 338
18.6 References 339
Chapter 19 Arterial Tree Morphometry 342
19.1 Introduction 342
19.2 Data Acquisition for Vascular Morphometry 344
19.3 Image Processing for Arterial Tree Morphometry 346
19.4 Arterial Tree Morphometry in Pulmonary Hypertension Research 350
19.5 Discussion and Conclusions 358
19.6 References 360
Chapter 20 Image-Based Computational Biomechanics of the Musculoskeletal System 366
20.1 Introduction 366
20.2 Three-Dimensional Biomechanical Models of the Musculoskeletal System 367
20.3 Bone Structure and Material Property Analysis 372
20.4 Applications 376
20.5 Summary 378
20.6 References 378
Chapter 21 Three-Dimensional Bone Angle Quantification 380
21.1 Introduction 380
21.2 3D Angle Measurement Method 383
21.3 Results 386
21.4 Discussion 388
21.5 References 389
Chapter 22 Database Selection and Feature Extraction for Neural Networks 392
22.1 Introduction 392
22.2 Database Selection 395
22.3 Feature Selection 398
22.4 Summary 402
22.5 References 403
Chapter 23 Quantitative Image Analysis for Estimation of Breast Cancer Risk 406
23.1 Introduction 406
23.2 Methods for Characterizing Mammographic Density 410
23.3 Planimetry 411
23.4 Semiautomated Feature: Interactive Thresholding 411
23.5 Automated Analysis of Mammographic Densities 413
23.6 Symmetry of Projection in the Quantitative Analysis of Mammographic Images 416
23.7 Variation of Thickness across the Breast: Effect on Density Analysis 416
23.8 Volumetric Analysis of Mammographic Density 418
23.9 Other Imaging Modalities 420
23.10 Applications of Mammographic Density Measurements 420
23.11 References 421
Chapter 24 Classification of Breast Lesions from Mammograms 424
24.1 Techniques for Classifying Breast Lesions 425
24.2 Performance of Computer Classification 432
24.3 Effect of Computer Classification on Radiologists' Diagnostic Performance 433
24.4 Methods for Presenting Computer Analysis to Radiologists 435
24.5 Summary 439
24.6 References 439
Chapter 25 Quantitative Analysis of Cardiac Function 444
25.1 Dynamic Image Acquisition Techniques 444
25.2 Dynamic Analysis of Left Ventricular Function 445
25.3 Quantitative Evaluation of Flow Motion 453
25.4 Conclusion 456
25.5 References 456
Chapter 26 Image Processing and Analysis in Tagged Cardiac MRI 460
26.1 Introduction 460
26.2 Background 461
26.3 Feature Tracking Techniques in MR Tagging 464
26.4 Direct Encoding Methods 469
26.5 3-D Motion Estimation 471
26.6 Discussion 474
26.7 References 475
Chapter 27 Cytometric Features of Fluorescently Labeled Nuclei for Cell Classification 478
27.1 Introduction 478
27.2 Nuclear Features 480
27.3 Classification Process 483
27.4 Example of Feature Analysis for Classification 485
27.5 Conclusion 486
27.6 References 487
Chapter 28 Image Interpolation and Resampling 490
28.1 Introduction 490
28.2 Classical Interpolation 492
28.3 Generalized Interpolation 493
28.4 Terminology and Other Pitfalls 494
28.5 Artifacts 494
28.6 Desirable Properties 495
28.7 Approximation Theory 498
28.8 Specific Examples 502
28.9 Cost-Performance Analysis 508
28.10 Experiments 510
28.11 Conclusion 513
28.12 References 517
Part IV Registration 520
Chapter 29 The Physical Basis of Spatial Distortions in Magnetic Resonance Images 524
29.1 Introduction 524
29.2 Review of Image Formation 524
29.3 Hardware Imperfections 526
29.4 Effects of Motion 530
29.5 Chemical Shift Effects 532
29.6 Imperfect MRI Pulse Sequences 533
29.7 fMRI Artifacts 536
29.8 Concluding Remarks 537
29.9 References 538
Chapter 30 Physical and Biological Bases of Spatial Distortions in Positron Emission Tomography Images 540
30.1 Introduction 540
30.2 Physical Distortions of PET 540
30.3 Anatomical Distortions 543
30.4 Methods of Correction 545
30.5 Summary 547
30.6 References 547
Chapter 31 Biological Underpinnings of Anatomic Consistency and Variability in the Human Brain 550
31.1 Introduction 550
31.2 Cerebral Anatomy at the Macroscopic Level 551
31.3 Cerebral Anatomical Variability 555
31.4 Anatomical Variability and Functional Areas 559
31.5 Conclusion 562
31.6 References 562
Chapter 32 Spatial Transformation Models 566
32.1 Homogeneous Coordinates 566
32.2 Rigid-Body Model 567
32.3 Global Rescaling Transformation 580
32.4 Nine-Parameter Affine Model 583
32.5 Other Special Constrained Affine Transformations 586
32.6 General Affine Model 586
32.7 Perspective Transformations 588
32.8 Spatial Transformations Using Quaternions 589
32.9 Nonlinear Spatial Transformation Models 591
32.10 Averaging Spatial Transformations 592
32.11 References 593
Chapter 33 Validation of Registration Accuracy 594
33.1 Units for Reporting Registration Errors 594
33.2 Validation by Visual Inspection 595
33.3 Validation of Point Fiducial-Based Registration and Cross-Validation Using External Fiducial Gold Standards 596
33.4 Cross-Validation 597
33.5 Sensitivity to Starting Parameters and Statistical Modeling 597
33.6 Simulations 598
33.7 Phantoms and Cadavers 598
33.8 Internal Consistency 598
33.9 Validation of Intersubject Warping 598
33.10 References 599
Chapter 34 Landmark-Based Registration Using Features Identified through Differential Geometry 602
34.1 Feature Extraction: Extremal Points and Lines 602
34.2 Rigid Registration 606
34.3 Robustness and Uncertainty Analysis 609
34.4 Conclusion 614
34.5 References 614
Chapter 35 Image Registration Using Chamfer Matching 616
35.1 Theory 616
35.2 Medical Applications of Chamfer Matching 618
35.3 Performance Tests of the Chamfer Matching Algorithm 624
35.4 Conclusions 626
35.5 References 627
Chapter 36 Within-Modality Registration Using Intensity-Based Cost Functions 630
36.1 Cost Functions for Intramodality Registration 630
36.2 Interpolation Method 632
36.3 Calculus-Based Optimization 632
36.4 Speed and Accuracy Trade-offs 634
36.5 Scope and Limitations 634
36.6 Future Directions 635
36.7 References 635
Chapter 37 Across-Modality Registration Using Intensity-Based Cost Functions 638
37.1 Introduction 638
37.2 Background to the Use of Voxel Similarity Measures 639
37.3 Joint Histograms 640
37.4 Information Theory Measures 642
37.5 Optimization 644
37.6 Applications of Mutual Information 646
37.7 Criticisms of Mutual Information 647
37.8 Conclusions 648
37.9 References 652
Chapter 38 Talairach Space as a Tool for Intersubject Standardization in the Brain 654
38.1 Spatial Normalization 654
38.2 General Spatial Normalization Algorithm 656
38.3 Feature Matching 656
38.4 Transformation 657
38.5 Talairach Atlases 657
38.6 Manual SN Example 657
38.7 Accuracy of Spatial Normalization 660
38.8 Transformed Image Standardization 662
38.9 Anatomical and Functional Variability 663
38.10 Uses in Functional and Anatomical Studies 664
38.11 References 664
Chapter 39 Warping Strategies for Intersubject Registration 668
39.1 Challenges in 3D Brain Imaging 668
39.2 Classification of Warping Algorithms 670
39.3 Cortical Pattern Matching 679
39.4 Pathology Detection 685
39.5 Conclusion 693
39.6 References 693
Chapter 40 Optimizing MR Image Resampling 700
40.1 Introduction 700
40.2 Conservation of Information 701
40.3 Resampling by Full Fourier Transform Methods 704
40.4 Three-dimensional Resampling of Slices 708
40.5 Conclusions 708
40.6 References 709
Chapter 41 Clinical Applications of Image Registration 710
41.1 Introduction 710
41.2 Intramodality Registration 710
41.3 Intermodality (or Multimodality) Registration 713
41.4 Intersubject Registration 716
41.5 Conclusion 717
41.6 References 718
Chapter 42 Registration for Image-Guided Surgery 720
42.1 Introduction 720
42.2 Image-Guided Neurosurgery System 721
42.3 Operating Room Procedure 725
42.4 Performance Analysis 725
42.5 Operating Room Results 725
42.6 Summary 728
42.7 References 728
Chapter 43 Image Registration and the Construction of Multidimensional Brain Atlases 732
43.1 Introduction 732
43.2 Structure of a Brain Atlas 733
43.3 Types of Atlases 733
43.4 Coordinate Systems 734
43.5 Registration 736
43.6 Deformable Brain Atlases 737
43.7 Warping 738
43.8 Multiple Modalities and Dimensions 740
43.9 Atlases of Cortical Patterns 740
43.10 Disease States 742
43.11 Dynamic Brain Atlases 742
43.12 Conclusion 743
43.13 References 744
Part V Visualization 750
Chapter 44 Visualization Pathways in Biomedicine 754
44.1 Visualization in Medicine 754
44.2 Illustrative Visualization 756
44.3 Investigative Visualization 760
44.4 Imitative Visualization 766
44.5 Visualization in Biology 768
44.6 Visualization in Spatial Biostatistics 769
44.7 Parametric Visualization 770
44.8 Discussion 771
44.9 References 776
Chapter 45 Three-Dimensional Visualization in Medicine and Biology 780
45.1 Introduction 780
45.2 Background 781
45.3 Methods 782
45.4 Applications 791
45.5 Discussion 805
45.6 References 807
Chapter 46 Volume Visualization in Medicine 810
46.1 Introduction 810
46.2 Volumetric Data 811
46.3 Rendering via Geometric Primitives 811
46.4 Direct Volume Rendering: Prelude 812
46.5 Volumetric Function Interpolation 812
46.6 Volume Rendering Techniques 814
46.7 Acceleration Techniques 821
46.8 Classification and Transfer Functions 824
46.9 Volumetric Global Illumination 826
46.10 Making Volume Rendering Interactive 827
46.11 Multi-Channel and Multi-Modal Data 830
46.12 Illustrative and Task-Driven Volume Rendering 831
46.13 Case Study: 3D Virtual Colonoscopy 832
46.14 References 834
Chapter 47 Fast Isosurface Extraction Methods for Large Image Data sets 842
47.1 Introduction 842
47.2 Accelerated Search 843
47.3 View-Dependent Algorithm 846
47.4 Real-Time Ray-Tracing 850
47.5 Sample Applications 852
47.6 References 853
Chapter 48 Computer Processing Methods for Virtual Endoscopy 858
48.1 Overview of Virtual Endoscopy 858
48.2 Computer Processing Methods for Virtual Endoscopy 859
48.3 Centerline Extraction and Flight Path Planning 860
48.4 Unfolding 860
48.5 Registration 860
48.6 Stool Tagging and Removal in Virtual Colonoscopy 863
48.7 Computer-Aided Detection 863
48.8 Conclusions 866
48.9 References 866
Part VI Compression, Storage, and Communication 872
Chapter 49 Fundamentals and Standards of Compression and Communication 874
49.1 Introduction 874
49.2 Compression and Decompression 876
49.3 Telecommunications 881
49.4 Conclusion 884
49.5 References 885
Chapter 50 Medical Image Archive, Retrieval, and Communication 886
50.1 Introduction 886
50.2 Medical Image Information Model 887
50.3 Medical Image Archive System 887
50.4 DICOM Image Communication Standard 888
50.5 Archive Software Components 890
50.6 HIS/RIS Interfacing and Image Prefetching 892
50.7 DICOM Image Archive Standard 893
50.8 Structured Reporting 895
50.9 HIPAA Compliance 896
50.10 Electronic Health Record 897
50.11 PACS in Telemedicine 897
50.12 PACS Research Applications 897
50.13 Summary 898
50.14 References 898
Chapter 51 Image Standardization in PACS 899
51.1 Introduction 899
51.2 Background Removal 900
51.3 Improvement of Visual Perception 907
51.4 Image Orientation 908
51.5 Accuracy of Quantitative Measurements in Image Intensifier Systems 911
51.6 Implementation of Image Standardization Functions in HI-PACS 915
51.7 Summary 917
51.8 References 917
Chapter 52 Imaging and Communication in Medical and Public Health Informatics: Current Issues and Emerging Trends 920
52.1 Introduction 920
52.2 Imaging and Communication in Medical Informatics 921
52.3 Imaging and Communication in Public Health Informatics 923
52.4 Discussion 929
52.5 Conclusion and Future Trends 929
52.6 References 930
Chapter 53 Dynamic Mammogram Retrieval from Web-based Image Libraries Using Multiagents 933
53.1 Introduction 933
53.2 Related Works 934
53.3 Methods 934
53.4 Retrieval Strategy 935
53.5 Experiments 936
53.6 Discussion 939
53.7 Conclusion 941
53.8 References 941
Chapter 54 Quality Evaluation for Compressed Medical Images: Fundamentals 942
54.1 Introduction 942
54.2 Image Compression 943
54.3 The Three Data Sets 944
54.4 Average Distortion and SNR 946
54.5 Subjective Ratings 949
54.6 Diagnostic Accuracy and ROC Methodology 952
54.7 Determination of a Gold Standard 955
54.8 Concluding Remarks 956
54.9 References 956
Chapter 55 Quality Evaluation for Compressed Medical Images: Statistical Issues 959
55.1 Introduction 959
55.2 Statistical Size and Power 959
55.3 Analysis of Learning Effects 961
55.4 Comparison of Judges 962
55.5 Relationships Between Quality Measures 963
55.6 Philosophical Issues 967
55.7 References 968
Chapter 56 Quality Evaluation for Compressed Medical Images: Diagnostic Accuracy 969
56.1 Introduction 969
56.2 CT Study: Example of Detection Accuracy 969
56.3 MR Study: Example of Measurement Accuracy 974
56.4 Mammography Study: Example of Management Accuracy 980
56.5 Concluding Remarks 985
56.6 References 986
Chapter 57 Three-Dimensional Image Compression with Wavelet Transforms 988
57.1 Background 988
57.2 Wavelet Theory 988
57.3 Three-Dimensional Image Compression with Wavelet Transform 990
57.4 Wavelet Filter Selection for a 3D Image Data Set 992
57.5 References 997
Index 998
Contributors
M. Stella Atkins
School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
Alan H. Barr
Computer Graphics Laboratory, Department of Computer Science, Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA 91125
James C. Bezdek
Computer Science Department, University of West Florida, Pensacola, FL 32514
Thierry Blu
Swiss Federal Institute of Technology-Lausanne, EPFL/DMT/IOA/Biomedical Imaging Group, CH-1015 Lausanne, Switzerland
Norman F. Boyd
Division of Clinical Epidemiology and Biostatistics, Ontario Cancer Institute, Toronto, ON, Canada
Tobias C. Cahoon
Computer Science Department, University of West Florida, Pensacola, FL 32514
Pamela Cosman
Department of Electrical and Computer Engineering, University of California at San Diego, La Jolla, CA 92093-0407
Chapters 54, 55, 56
Fabrice Crivello
Groupe d’Imagerie Neurofonctionelle (GIN), Université de Caen, GIP Cyceron, 14074 Caen Cedex, France
Magnus Dahlbom
Division of Nuclear Medicine, Department of Molecular and Medical Pharmacology, UCLA School of Medicine, Los Angeles, CA 90095-6942
Quentin E. Dolecek
Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723
James S. Duncan
Image Processing and Analysis Group, Departments of Diagnostic Radiology and Electrical Engineering, Yale University, New Haven, CT 06520-8042
William F. Eddy
Department of Statistics, Carnegie Mellon University, Pittsburgh, PA 15213
John J. Elias
Biomechanics Laboratory, Medical Education and Research Institute of Colorado
Peter T. Fox
Research Imaging Center, University of Texas Health Science Center at San Antonio, San Antonio, TX 78232
Alberto F. Goldszal
Imaging Sciences Program, Clinical Center, National Institutes of Health Bethesda, MD 20892
Eric Grimson
Artifical Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139
Nobuhiko Hata
Surgical Planning Laboratory, Department of Radiology, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA 02115
David J. Hawkes
Radiological Sciences, King’s College London, Guy’s Hospital, London SE1 9RT, United Kingdom
Derek L.G. Hill
Radiological Sciences, King’s College London, Guy’s Hospital, London SE1 9RT, United Kingdom
Sung-Cheng (Henry) Huang
Division of Nuclear Medicine, Department of Molecular and Medical Pharmacology, UCLA School of Medicine, Los Angeles, CA 90095-6942
H.K. Huang
Department of Radiology, Childrens Hospital of Los Angeles/University of Southern California, Los Angeles, CA 90027
Walter Huda
Director, Radiological Physics, Department of Radiology, SUNY Upstate Medical University, Syracuse, NY 13210
Peter Jezzard
FMRIB Centre, Department of Clinical Neurology, University of Oxford, United Kingdom
Christopher R. Johnson
Center for Scientific Computing and Imaging, Department of Computer Science, University of Utah, Salt Lake City, UT 84112
Marc Joliot
Groupe d’Imagerie Neurofonctionelle (GIN), Université de Caen, GIP Cyceron, 14074 Caen Cedex, France
Arie E. Kaufman
Department of Computer Science, State University of New York at Stony Brook, Stony Brook, NY 11794-4400
Ron Kikinis
Department of Radiology, Harvard Medical School, Brigham & Women’s Hospital, Boston MA, USA
Chapters 2, 21, 42
Robert Knowlton
Epilepsy Center, University of Alabama School of Medicine, Birmingham, AL 35205
Hans Knutsson
Department of Electrical Engineering, Linköping University, Computer Vision Laboratory, Linköping, Sweden
Jens Kordelle
Klinik und Poliklinik für Orthopädie, und Orthopädische Chirurgie, Universitätsklinikum Giessen, Germany
Vassili A. Kovalev
Centre for Vision, Speech and Signal, Processing, University of Surrey, Guildford GU2 7XH, United Kingdom
Chapter 14, 16
Andrew Laine
Department of Biomedical Engineering, Columbia University, New York, NY 10027
Jack L. Lancaster
Research Imaging Center, University of Texas Health Science Center at San Antonio, San Antonio, TX 78232
Yarden Livnat
Center for Scientific Computing and Imaging, Department of Computer Science, University of Utah, Salt Lake City, UT 84112
Shyh-Liang Lou
Laboratory for Radiological Informatics, Department of Radiology, University of California at San Francisco, San Francisco, CA 94903
Blair T. Mackiewich
School of Computing Science, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
Bernard Mazoyer
Groupe d’Imagerie Neurofonctionelle (GIN), Université de Caen, GIP Cyceron, 14074 Caen Cedex, France
Tim McInerney
Department of Computer Science, Ryerson University, Toronto, ON M5S 3H5, Canada
Michael B. Millis
Department of Orthopaedic Surgery, Children’s Hospital, Boston, MA 02115
Nael F. Osman
Center for Imaging Science, Department of Radiology, Johns Hopkins University Baltimore, MD 21287
Raman B. Paranjape
Electronic Systems Engineering, University of Regina, Regina, SASK S4S 0A2, Canada
Chapter 1, 53
Steven G. Parker
Center for Scientific Computing and Imaging, Department of Computer Science, University of Utah, Salt Lake City, UT 84112
| Erscheint lt. Verlag | 24.12.2008 |
|---|---|
| Sprache | englisch |
| Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
| Medizin / Pharmazie ► Allgemeines / Lexika | |
| Medizin / Pharmazie ► Pflege | |
| Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie | |
| Naturwissenschaften ► Biologie | |
| Technik ► Bauwesen | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 0-08-055914-X / 008055914X |
| ISBN-13 | 978-0-08-055914-8 / 9780080559148 |
| Haben Sie eine Frage zum Produkt? |
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EPUB (Adobe DRM)Größe: 55,7 MB
Kopierschutz: Adobe-DRM
Adobe-DRM ist ein Kopierschutz, der das eBook vor Mißbrauch schützen soll. Dabei wird das eBook bereits beim Download auf Ihre persönliche Adobe-ID autorisiert. Lesen können Sie das eBook dann nur auf den Geräten, welche ebenfalls auf Ihre Adobe-ID registriert sind.
Details zum Adobe-DRM
Dateiformat: EPUB (Electronic Publication)
EPUB ist ein offener Standard für eBooks und eignet sich besonders zur Darstellung von Belletristik und Sachbüchern. Der Fließtext wird dynamisch an die Display- und Schriftgröße angepasst. Auch für mobile Lesegeräte ist EPUB daher gut geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen eine
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen eine
Geräteliste und zusätzliche Hinweise
Zusätzliches Feature: Online Lesen
Dieses eBook können Sie zusätzlich zum Download auch online im Webbrowser lesen.
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
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