Biomedical image processing:
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| Sprache: | English |
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Berlin
Springer
[2011]
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| Schriftenreihe: | Biological and medical physics, biomedical engineering
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| Beschreibung: | Literaturangaben |
| Beschreibung: | XXXVII, 595 Seiten Illustrationen, Diagramme 25 cm |
| ISBN: | 9783642158155 |
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| 245 | 1 | 0 | |a Biomedical image processing |c Thomas M. Deserno, editor |
| 264 | 1 | |a Berlin |b Springer |c [2011] | |
| 300 | |a XXXVII, 595 Seiten |b Illustrationen, Diagramme |c 25 cm | ||
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Datensatz im Suchindex
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| adam_text | IMAGE 1
CONTENTS
1 FUNDAMENTALS OF BIOMEDICAL IMAGE PROCESSING THOMAS M. DESERNO 1
1.1 INTRODUCTION 1
1.1.1 STEPS OF IMAGE PROCESSING 2
1.1.2 REMARKS ON TERMINOLOGY 3
1.1.3 BIOMEDICAL IMAGE PROCESSING 4
1.2 MEDICAL IMAGE FORMATION 4
1.2.1 BASIC PHYSICS 5
1.2.2 IMAGING MODALITIES 6
1.2.3 DIGITALIZATION 13
1.3 IMAGE ENHANCEMENT 16
1.3.1 HISTOGRAM TRANSFORMS 16
1.3.2 CONVOLUTION 18
1.3.3 MATHEMATICAL MORPHOLOGY 18
1.3.4 CALIBRATION 19
1.3.5 REGISTRATION 20
1.4 IMAGE DATA VISUALIZATION 22
1.4.1 MARCHING CUBE ALGORITHM 23
1.4.2 SURFACE RENDERING 23
1.4.3 VOLUME RENDERING 23
1.5 VISUAL FEATURE EXTRACTION 25
1.5.1 DATA LEVEL 25
1.5.2 PIXEL LEVEL 25
1.5.3 EDGE LEVEL 25
1.5.4 TEXTURE LEVEL 26
1.5.5 REGION LEVEL 26
1.6 SEGMENTATION 27
1.6.1 PIXEL-BASED SEGMENTATION 27
1.6.2 EDGE-BASED SEGMENTATION 30
1.6.3 REGION-BASED SEGMENTATION 31
BIBLIOGRAFISCHE INFORMATIONEN HTTP://D-NB.INFO/1005646554
DIGITALISIERT DURCH
IMAGE 2
XII CONTENTS
1.6.4 OVER- AND UNDER-SEGMENTATION 32
1.6.5 MODEL-BASED SEGMENTATION 34
1.7 CLASSIFICATION 37
1.7.1 STATISTIC CLASSIFIERS 39
1.7.2 SYNTACTIC CLASSIFIERS 39
1.7.3 COMPUTATIONAL INTELLIGENCE-BASED CLASSIFIERS 40 1.8 QUANTITATIVE
MEASUREMENTS AND INTERPRETATION 41
1.8.1 PARTIAL VOLUME EFFECT 42
1.8.2 EUCLIDEAN PARADIGM 42
1.8.3 SCENE ANALYSIS 42
1.8.4 EXAMPLES ... 43
1.9 IMAGE MANAGEMENT 45
1.9.1 ARCHIVING 45
1.9.2 COMMUNICATION 45
1.9.3 RETRIEVAL 47
1.10 CONCLUSION AND OUTLOOK 48
REFERENCES 49
PART I IMAGE FORMATION
2 FUSION OF PET AND MRI FOR HYBRID IMAGING ZANG-HEE CHO, YOUNG-DON SON,
YOUNG-BO KIM, AND SEUNG-SCHIK YOO 55
2.1 INTRODUCTION 55
2.2 POSITRON EMISSION TOMOGRAPHY 57
2.2.1 BASIC PRINCIPLES 57
2.2.2 IMAGE RECONSTRUCTION 59
2.2.3 SIGNAL OPTIMIZATION 59
2.2.4 HIGH-RESOLUTION RESEARCH TOMOGRAPH 60
2.3 MAGNETIC RESONANCE IMAGING 62
2.3.1 BASIC PRINCIPLES 62
2.3.2 IMAGE RECONSTRUCTION 63
2.3.3 SIGNAL OPTIMIZATION 64
2.3.4 HIGH-FIELD MRI 65
2.4 HYBRID PET FUSION SYSTEM 67
2.4.1 PET/CT SYSTEMS 68
2.4.2 PET/MRI SYSTEMS 68
2.4.3 HIGH-RESOLUTION FUSION 70
2.4.4 PET/MRI FUSION ALGORITHM 72
2.5 CONCLUSIONS 76
REFERENCES 76
IMAGE 3
CONTENTS XIII
3 CARDIAC 4D ULTRASOUND IMAGING JAN D HOOGE 81
3.1 THE ROLE OF ULTRASOUND IN CLINICAL CARDIOLOGY 81
3.2 PRINCIPLES OF ULTRASOUND IMAGE FORMATION 82
3.2.1 THE PULSE-ECHO MEASUREMENT 82
3.2.2 GRAY SCALE ENCODING 83
3.2.3 GRAY SCALE IMAGING 85
3.2.4 PHASED ARRAY TRANSDUCER TECHNOLOGY 85
3.3 LIMITATIONS OF 2D CARDIAC ULTRASOUND 86
3.3.1 COMPLEX ANATOMY (CONGENITAL HEART DISEASE) 87
3.3.2 GEOMETRIC ASSUMPTIONS TO ASSESS VOLUMES 88
3.3.3 OUT-OF-PLANE MOTION AND FORESHORTENING 89
3.4 APPROACHES TOWARDS 3D CARDIAC ULTRASOUND 89
3.4.1 FREEHAND 3D ULTRASOUND 90
3.4.2 PROSPECTIVE GATING 90
3.4.3 RETROSPECTIVE GATING 91
3.4.4 TWO-DIMENSIONAL ARRAYS 92
3.5 VALIDATION OF 3D CARDIAC ULTRASOUND METHODOLOGIES 95
3.6 EMERGING TECHNOLOGIES 96
3.6.1 TRANSESOPHAGEAL 3D IMAGING 96
3.6.2 TRUE REAL-TIME VOLUMETRIC IMAGING 97
3.7 REMAINING CHALLENGES IN 4D CARDIAC ULTRASOUND 98
3.7.1 RESOLUTION 98
3.7.2 IMAGE QUALITY 99
3.7.3 DATA VISUALIZATION AND INTERACTION 101
3.7.4 SEGMENTATION/AUTOMATED ANALYSIS 101
REFERENCES 102
PART II IMAGE ENHANCEMENT
4 MORPHOLOGICAL IMAGE PROCESSING APPLIED IN BIOMEDICINE ROBERTO A.
LOTUFO, LETICIA RITTNER, ROMARIC AUDIGIER, RUBENS C. MACHADO, AND ANDRE
V. SAUDE 107
4.1 INTRODUCTION 107
4.2 BINARY MORPHOLOGY 108
4.2.1 EROSION AND DILATION 108
4.2.2 OPENING AND CLOSING 110
4.2.3 MORPHOLOGICAL RECONSTRUCTION FROM MARKERS I LL
4.2.4 RECONSTRUCTION FROM OPENING 112
4.3 GRAY-SCALE OPERATIONS 114
4.3.1 EROSION AND DILATION 115
4.3.2 OPENING AND CLOSING 116
4.3.3 COMPONENT FILTERS AND MORPHOLOGICAL RECONSTRUCTION.... 119 4.3.4
REGIONAL MAXIMA 121
IMAGE 4
XIV CONTENTS
4.4 WATERSHED SEGMENTATION 122
4.4.1 CLASSICAL WATERSHED TRANSFORM 122
4.4.2 FILTERING THE MINIMA 123
4.4.3 WATERSHED FROM MARKERS 124
4.4.4 INNER AND OUTER MARKERS 125
4.5 SEGMENTATION OF DIFFUSION MRI 126
4.6 CONCLUSIONS 128
REFERENCES 128
5 MEDICAL IMAGE REGISTRATION DANIEL RUECKERT AND JULIA A. SCHNABEL 131
5.1 INTRODUCTION 131
5.2 TRANSFORMATION MODEL 132
5.2.1 RIGID TRANSFORMATION 133
5.2.2 AFFINE TRANSFORMATION 133
5.2.3 PROJECTIVE TRANSFORMATION 134
5.2.4 NON-RIGID TRANSFORMATION: PARAMETRIC MODELS 134 5.2.5 NON-RIGID
TRANSFORMATION: NON-PARAMETRIC MODELS 138 5.3 REGISTRATION BASIS 139
5.3.1 FEATURE-BASED REGISTRATION 140
5.3.2 VOXEL-BASED REGISTRATION 141
5.4 OPTIMIZATION 144
5.5 VALIDATION OF REGISTRATION 144
5.6 APPLICATION 146
5.6.1 INTRA-SUBJECT REGISTRATION 146
5.6.2 INTER-SUBJECT REGISTRATION 147
5.7 SUMMARY AND CONCLUSIONS 149
REFERENCES 150
PART III FEATURE EXTRACTION AND SELECTION
6 TEXTURE IN BIOMEDICAL IMAGES MARIA PETROU 157
6.1 INTRODUCTION 157
6.2 CHARACTERIZING THE TEXTURE OF SWATCHES 158
6.2.1 FROM GRAMMARS TO MARKOV RANDOM FIELDS 158 6.2.2 FROM MARKOV RANDOM
FIELDS TO FRACTALS 159
6.2.3 FROM MARKOV RANDOM FIELDS TO GIBBS DISTRIBUTIONS 159 6.2.4
CO-OCCURRENCE MATRICES 160
6.2.5 GENERALIZED CO-OCCURRENCE MATRICES 161
6.2.6 ORIENTATION HISTOGRAMS 162
6.2.7 TEXTONS 163
6.2.8 FEATURES FROM THE DISCRETE FOURIER TRANSFORM 163 6.3 SIMULTANEOUS
TEXTURE SEGMENTATION AND RECOGNITION 165
IMAGE 5
CONTENTS XV
6.3.1 FROM SPATIO-FREQUENCY TO SPATIO-STRUCTURAL SPACE 166 6.3.2
STATISTICAL SPATIO-STRUCTURAL SPACE 168
6.3.3 MONOGENIC SIGNAL 169
6.3.4 FROM MONOGENIC SIGNAL BACK TO GABOR FUNCTIONS 170 6.3.5 BEYOND
SPATIAL PATTERNS INTO GRAY VALUE DISTRIBUTIONS... 171 6.4 EXAMPLES OF
THE USE OF TEXTURE FEATURES IN BIOMEDICAL APPLICATIONS 172
6.4.1 MAMMOGRAPHY 172
6.4.2 BRAIN IMAGE DATA 173
6.5 DISCUSSION AND CONCLUSIONS 174
REFERENCES 175
7 MULTI-SCALE AND MULTI-ORIENTATION MEDICAL IMAGE ANALYSIS BART M. TER
HAAR ROMENY 177
7.1 INTRODUCTION 177
7.2 THE NECESSITY OF SCALE 178
7.2.1 THE OPTIMAL APERTURE FUNCTION 178
7.2.2 DERIVATIVES OF SAMPLED, DISCRETE DATA, SUCH AS IMAGES . .. 180 7.3
DIFFERENTIAL INVARIANTS 181
7.3.1 GAUGE COORDINATES 181
7.3.2 INVARIANTS FROM TENSOR CONTRACTION 182
7.4 SECOND ORDER IMAGE STRUCTURE AND FEATURES 183
7.4.1 ISOPHOTE CURVATURE 183
7.4.2 FLOWLINE CURVATURE 184
7.4.3 CORNERS 184
7.4.4 PRINCIPAL CURVATURES 185
7.4.5 THE SHAPE INDEX 186
7.5 THIRD ORDER IMAGE STRUCTURE: T-JUNCTIONS 187
7.6 ADAPTIVE BLURRING AND GEOMETRY-DRIVEN DIFFUSION 187
7.7 EDGE FOCUSING 189
7.8 ORIENTATION ANALYSIS 190
7.9 APPLICATIONS .192
7.9.1 CATHETER DETECTION 192
7.9.2 ENDOCARD CONTOUR DETECTION 193
7.9.3 DENOISING OF CROSSING LINES 193
7.10 CONCLUSION 194
REFERENCES 195
8 FEATURE EXTRACTION AND SELECTION FOR DECISION MAKING AGMA J.M. TRAINA,
CAETANO TRAINA JR., ANDRE G.R. BALAN, MARCELA X. RIBEIRO, PEDRO H.
BUGATTI, CAROLINA Y.V. WATANABE, AND PAULO M. AZEVEDO-MARQUES 197
8.1 INTRODUCTION 197
8.2 IMAGE REPRESENTATION 198
8.2.1 MEDICAL IMAGE SEGMENTATION AND FEATURE EXTRACTION . . .. 199 8.2.2
COLOR FEATURES 201
IMAGE 6
XVI CONTENTS
8.2.3 TEXTURE FEATURES 203
8.2.4 SHAPE FEATURES 204
8.3 IMAGE FEATURES AND DISTANCE FUNCTIONS 205
8.3.1 SIMILARITY SEARCH AND METRIC SPACES 206
8.3.2 DISTANCE FUNCTIONS 206
8.3.3 CASE STUDY: EVALUATING DISTANCE FUNCTIONS FOR SEPARATING DATA 208
8.4 FEATURE SELECTION 210
8.4.1 CURSE OF DIMENSIONALITY 211
8.4.2 TRADITIONAL ALGORITHM FOR FEATURE SELECTION 211 8.4.3 COMBINED
FEATURE SELECTION AND DISCRETIZATION 213 8.5 ASSOCIATION RULE MINING 215
8.5.1 DEFINITION 215
8.5.2 CASE STUDY: IMPROVING COMPUTER-AIDED DIAGNOSIS BY ASSOCIATION RULE
MINING 215
8.6 CONCLUSIONS 220
REFERENCES 221
PART IV SEGMENTATION
9 PARAMETRIC AND NON-PARAMETRIC CLUSTERING FOR SEGMENTATION HAYIT
GREENSPAN AND TANVEER SYEDA-MAHMOOD 227
9.1 INTRODUCTION 227
9.2 IMAGE MODELING AND SEGMENTATION 229
9.2.1 IMAGE MODELING 230
9.2.2 SEGMENTATION 230
9.2.3 STATE OF THE ART 231
9.3 PROBABILISTIC MODELING OF FEATURE SPACE 231
9.3.1 GAUSSIAN MIXTURE MODELS 232
9.3.2 EXPECTATION MAXIMIZATION 232
9.3.3 VISUALIZATION 233
9.4 USING GMMS FOR BRAIN TISSUE AND LESION SEGMENTATION 234 9.4.1
APPLICATION DOMAIN 234
9.4.2 SPATIAL CONSTRAINTS 234
9.4.3 MODELING SPATIAL CONSTRAINTS THROUGH GMM 235 9.4.4 TISSUE
SEGMENTATION 238
9.4.5 LESION SEGMENTATION 238
9.5 NON-PARAMETRIC CLUSTERING APPROACHES TO SEGMENTATION 240 9.5.1
DESCRIPTION OF THE FEATURE SPACE 241
9.5.2 CLUSTERING INTENSITY, GEOMETRY, AND MOTION 243 9.6 USING
NON-PARAMETRIC CLUSTERING FOR CARDIAC ULTRASOUND 245 9.6.1 APPLICATION
DOMAIN 245
9.6.2 CARDIAC MOTION ESTIMATION 246
9.6.3 SEGMENTATION OF MEANINGFUL REGIONS 246
IMAGE 7
CONTENTS XVII
9.7 DISCUSSION 248
REFERENCES 248
10 REGION-BASED SEGMENTATION: FUZZY CONNECTEDNESS, GRAPH CUT AND RELATED
ALGORITHMS KRZYSZTOF CHRIS CIESIELSKI AND JAYARAM K. UDUPA 251
10.1 INTRODUCTION AND OVERVIEW 251
10.1.1 DIGITAL IMAGE SCENE 252
10.1.2 TOPOLOGICAL AND GRAPH-THEORETICAL SCENE REPRESENTATIONS 253
10.1.3 DIGITAL IMAGE 253
10.1.4 DELINEATED OBJECTS 254
10.2 THRESHOLD-INDICATED FUZZY CONNECTED OBJECTS 254
10.2.1 ABSOLUTE FUZZY CONNECTEDNESS OBJECTS 255
10.2.2 ROBUSTNESS OF OBJECTS 256
10.2.3 ALGORITHM FOR DELINEATING OBJECTS 256
10.3 OPTIMIZATION IN FOREGROUND-BACKGROUND CASE 257
10.3.1 RELATIVE FUZZY CONNECTEDNESS 258
10.3.2 ALGORITHM FOR DELINEATING OBJECTS 259
10.3.3 GRAPH CUT DELINEATION 259
10.4 SEGMENTATION OF MULTIPLE OBJECTS 262
10.4.1 RELATIVE FUZZY CONNECTEDNESS 262
10.4.2 ITERATIVE RELATIVE FUZZY CONNECTEDNESS 263
10.4.3 ALGORITHM FOR ITERATIVE RELATIVE FUZZY CONNECTEDNESS . . .. 265
10.4.4 VARIANTS OF IRFC 266
10.5 SCALE-BASED AND VECTORIAL FUZZY CONNECTEDNESS 266
10.6 AFFINITY FUNCTIONS IN FUZZY CONNECTEDNESS 267
10.6.1 EQUIVALENT AFFINITIES 267
10.6.2 ESSENTIAL PARAMETERS IN AFFINITY FUNCTIONS 269
10.7 OTHER DELINEATION ALGORITHMS 270
10.7.1 GENERALIZED GRAPH CUT 270
10.7.2 LEVEL SET VS. GENERALIZED GRAPH CUT 271
10.8 MEDICAL IMAGE EXAMPLES 273
10.9 CONCLUDING REMARKS 276
REFERENCES 276
11 MODEL-BASED SEGMENTATION TOBIAS HEIMANN AND HERVE DELINGETTE 279
11.1 INTRODUCTION 279
11.2 DEFORMABLE SIMPLEX MESHES 281
11.2.1 INTERNAL FORCES ON SIMPLEX MESHES 282
11.2.2 IMAGE FORCES 283
11.2.3 GLOBALLY CONSTRAINED DEFORMATION 285
11.2.4 3D+T DEFORMABLE SIMPLEX MESHES 286
11.2.5 ADVANCED SEGMENTATION STRATEGIES 288
11.2.6 GEOMETRIC REPRESENTATIONS FOR MODEL-BASED SEGMENTATION 290
IMAGE 8
XVIII CONTENTS
11.3 STATISTICAL MODELS OF SHAPE AND APPEARANCE 291
11.3.1 SHAPE REPRESENTATION 292
11.3.2 POINT CORRESPONDENCE 292
11.3.3 CONSTRUCTION OF STATISTICAL SHAPE MODELS 295
11.3.4 MODELING OBJECT APPEARANCE 297
11.3.5 LOCAL SEARCH ALGORITHMS 298
11.4 CONCLUSION 300
REFERENCES 301
PART V CLASSIFICATION AND MEASUREMENTS
12 MELANOMA DIAGNOSIS ALEXANDER HORSCH 307
12.1 THE CUTANEOUS MELANOMA 307
12.1.1 MEDICAL BASICS 307
12.1.2 RELEVANCE OF EARLY DIAGNOSIS 309
12.2 STATE OF THE ART IN CM DIAGNOSIS 309
12.2.1 DIAGNOSTIC ALGORITHMS 309
12.2.2 IMAGING TECHNIQUES 311
12.2.3 DIAGNOSTIC ACCURACIES 313
12.3 DERMOSCOPY IMAGE ANALYSIS 314
12.3.1 IMAGE ANALYSIS APPROACHES 314
12.3.2 SEGMENTATION OF SKIN LESIONS 315
12.3.3 FEATURE EXTRACTION 316
12.3.4 FEATURE VISUALIZATION 317
12.3.5 CLASSIFICATION METHODS 319
12.4 COMMERCIAL SYSTEMS 322
12.4.1 SYSTEM DESIGN PRINCIPLES 322
12.4.2 IMAGE CAPTURE DEVICES 323
12.4.3 DERMOSCOPY COMPUTER SYSTEMS 324
12.5 EVALUATION ISSUES 324
12.5.1 CASE DATABASES 325
12.5.2 EVALUATION METHODS 325
12.6 CONCLUSION 325
REFERENCES 326
13 CADX MAMMOGRAPHY LENA COSTARIDOU 329
13.1 INTRODUCTION 329
13.2 BASIC TERMS AND DEFINITIONS 330
13.2.1 BREAST IMAGING MODALITIES 330
13.2.2 MAMMOGRAPHIE LESIONS 331
13.2.3 CADE SCHEMES 332
13.2.4 CADX ARCHITECTURES 333
IMAGE 9
CONTENTS XIX
13.3 CADX SCHEMES IN X-RAY MAMMOGRAPHY 335
13.3.1 MORPHOLOGY ANALYSIS OF MC CLUSTERS 335
13.3.2 TEXTURE ANALYSIS OF MC CLUSTERS 338
13.3.3 MORPHOLOGY AND TEXTURE ANALYSIS OF MASSES 339
13.4 CADX SCHEMES IN BREAST ULTRASOUND 341
13.5 CADX SCHEMES IN BREAST MRI 344
13.6 APPLICATION EXAMPLES 346
13.6.1 SEGMENTATION ACCURACY ON MC CLUSTER CONTENT 346 13.6.2
HETEROGENEITY OF ENHANCEMENT KINETICS IN DCE-MRI 349 13.7 DISCUSSION AND
CONCLUSIONS 351
REFERENCES 353
14 QUANTITATIVE MEDICAL IMAGE ANALYSIS FOR CLINICAL DEVELOPMENT OF
THERAPEUTICS MOSTAFA ANALOUI 359
14.1 INTRODUCTION 359
14.2 KEY ISSUES IN DRUG RESEARCH AND CLINICAL DEVELOPMENT 361 14.2.1
BIOLOGICAL MARKER 361
14.2.2 IMAGING MODALITY 362
14.3 QUANTITATIVE IMAGE ANALYSIS 363
14.3.1 ASSESSMENT OF OSTEOARTHRITIS 364
14.3.2 ASSESSMENT OF CAROTID ATHEROSCLEROSIS 365
14.3.3 ASSESSMENT OF CANCER 367
14.4 MANAGING VARIABILITY IN IMAGING BIOMARKERS 369
14.4.1 TECHNICAL VALIDATION 370
14.4.2 STANDARD OPERATION PROCEDURES 371
14.4.3 REGULATORY ISSUES 372
14.5 FUTURE DIRECTIONS 373
REFERENCES 374
PART VI IMAGE DATA VISUALIZATION
15 VISUALIZATION AND EXPLORATION OF SEGMENTED ANATOMIC STRUCTURES DIRK
BARTZ AND BERNHARD PREIM 379
15.1 INTRODUCTION 379
15.2 INDIRECT AND DIRECT VOLUME RENDERING 380
15.2.1 INDIRECT VOLUME RENDERING 380
15.2.2 RENDERING OF MULTIPLE OBJECTS 380
15.2.3 DIRECT VOLUME RENDERING 382
15.2.4 RENDERING OF SEGMENTED DATA 383
15.2.5 DISCUSSION 384
15.3 GENERATION OF SMOOTH AND ACCURATE SURFACE MODELS 386
15.3.1 MESH SMOOTHING WITH FAIRING 386
15.3.2 IMPROVING MESH QUALITY 388
IMAGE 10
XX CONTENTS
15.4 VISUALIZATION OF VASCULAR STRUCTURES 389
15.4.1 SURFACE-BASED VESSEL VISUALIZATION 390
15.4.2 MODEL-BASED SURFACE VISUALIZATION OF VASCULAR STRUCTURES 390
15.4.3 VOLUME RENDERING OF VASCULAR STRUCTURES 392 15.5 VIRTUAL
ENDOSCOPY 394
15.5.1 GRAPHICAL REPRESENTATION 395
15.5.2 INTERACTION MODEL 396
15.5.3 USER INTERFACE 396
15.5.4 CASE STUDY: VIRTUAL COLONOSCOPY 397
15.6 CONCLUSIONS 397
REFERENCES 398
16 PROCESSING AND VISUALIZATION OF DIFFUSION MRI JAMES G. MALCOLM,
YOGESH RATHI, AND CARL-FREDRIK WESTIN 403 16.1 INTRODUCTION 403
16.2 MODELING 404
16.2.1 IMAGING THE TISSUE 404
16.2.2 PARAMETRIC MODELS 405
16.2.3 NON-PARAMETRIC MODELS 405
16.2.4 REGULARIZARON 407
16.2.5 CHARACTERIZING TISSUE 407
16.3 TRACTOGRAPHY 408
16.3.1 DETERMINISTIC TRACTOGRAPHY 408
16.3.2 PROBABILISTIC TRACTOGRAPHY 409
16.3.3 GLOBAL TRACTOGRAPHY 411
16.3.4 VALIDATION 412
16.4 APPLICATIONS 413
16.4.1 VOLUME SEGMENTATION 413
16.4.2 FIBER CLUSTERING 414
16.4.3 CONNECTIVITY 416
16.4.4 TISSUE ANALYSIS 417
16.5 SUMMARY 418
REFERENCES 419
PART VII IMAGE MANAGEMENT AND INTEGRATION
17 DIGITAL IMAGING AND COMMUNICATIONS IN MEDICINE MICHAEL ONKEN, MARCO
EICHELBERG, JOERG RIESMEIER, AND PETER JENSCH ... 427 17.1 DICOM BASICS
427
17.1.1 INTRODUCTION AND OVERVIEW 428
17.1.2 INFORMATION OBJECTS 428
17.1.3 DISPLAY PIPELINE 430
17.1.4 NETWORK AND MEDIA SERVICES 433
17.1.5 CONFORMANCE 437
IMAGE 11
CONTENTS XXI
17.2 ADVANCED DICOM SERVICES 438
17.2.1 ADVANCED IMAGE DISPLAY SERVICES 438
17.2.2 DICOM STRUCTURED REPORTING 442
17.2.3 APPLICATION HOSTING 447
17.3 CONCLUSIONS AND OUTLOOK 452
REFERENCES 453
18 PACS-BASED COMPUTER-AIDED DETECTION AND DIAGNOSIS H.K. (BERNIE)
HUANG, BRENT J. LIU, ANH HONGTU LE, AND JORGE DOCUMET 455
18.1 INTRODUCTION 455
18.2 THE NEED FOR CAD-PACS INTEGRATION 456
18.2.1 APPROACHES OF CAD-PACS INTEGRATION 457
18.2.2 CAD SOFTWARE 459
18.3 DICOM STANDARD AND IHE WORKFLOW PROFILES 459
18.3.1 DICOM STRUCTURED REPORTING 460
18.3.2 IHE PROFILES 461
18.4 THE CAD-PACS* TOOLKIT 461
18.4.1 CONCEPT 462
18.4.2 STRUCTURE, COMPONENTS, AND EDITIONS 462
18.5 EXAMPLE OF CAD-PACS INTEGRATION 463
18.5.1 THE DIGITAL HAND ATLAS 463
18.5.2 CAD EVALUATION IN A LABORATORY SETTING 464
18.5.3 CAD EVALUATION IN A CLINICAL ENVIRONMENT 465
18.5.4 CAD-PACS INTEGRATION USING DICOM-SR 466
18.6 CONCLUSION 467
REFERENCES 469
19 CONTENT-BASED MEDICAL IMAGE RETRIEVAL HENNING MUELLER AND THOMAS M.
DESERNO 471
19.1 INTRODUCTION 471
19.1.1 MOTIVATION AND HISTORY 472
19.1.2 QUERY-BY-EXAMPLE(S) PARADIGM 472
19.2 GENERAL IMAGE RETRIEVAL 473
19.2.1 CLASSIFICATION VS. RETRIEVAL 473
19.2.2 SYSTEM COMPONENTS AND COMPUTATION 474
19.2.3 FEATURES AND SIGNATURES 474
19.2.4 DISTANCE AND SIMILARITY MEASURES 476
19.3 MEDICAL IMAGE RETRIEVAL 476
19.3.1 APPLICATION FIELDS 477
19.3.2 TYPES OF IMAGES 477
19.3.3 IMAGE PREPROCESSING 478
19.3.4 VISUAL AND NON-VISUAL IMAGE FEATURES 478
19.3.5 DATABASE ARCHITECTURES 479
19.3.6 USER INTERFACES AND INTERACTION 480
19.3.7 INTERFACING WITH CLINICAL INFORMATION SYSTEMS 480
IMAGE 12
XXII CONTENTS
19.4 EVALUATION 481
19.4.1 AVAILABLE DATABASES 481
19.4.2 TASKS AND USER MODELS 481
19.4.3 GROUND TRUTH AND GOLD STANDARDS 482
19.4.4 BENCHMARKS AND EVENTS 483
19.5 EXAMPLES FOR MEDICAL CBIR SYSTEMS 483
19.5.1 MEDICAL GNU IMAGE FINDING TOOL 484
19.5.2 IMAGE RETRIEVAL IN MEDICAL APPLICATIONS 484
19.6 DISCUSSION AND CONCLUSIONS 487
19.6.1 STRENGTHS AND WEAKNESSES OF CURRENT SYSTEMS 488 19.6.2 GAPS OF
MEDICAL CBIR SYSTEMS 488
19.6.3 FUTURE DEVELOPMENTS 488
REFERENCES 490
PART VIII EVALUATION AND CUSTOMIZING
20 SYSTEMATIC EVALUATIONS AND GROUND TRUTH JAYASHREE KALPATHY-CRAMER AND
HENNING MUELLER 497
20.1 INTRODUCTION 497
20.2 COMPONENTS FOR SUCCESSFUL EVALUATION CAMPAIGNS 498 20.2.1
APPLICATIONS AND REALISTIC TASKS 498
20.2.2 COLLECTIONS OF IMAGES AND GROUND TRUTH 499
20.2.3 APPLICATION-SPECIFIC METRICS 500
20.2.4 ORGANIZATIONAL RESOURCES AND PARTICIPANTS 501 20.3 EVALUATION
METRICS AND GROUND TRUTH 502
20.3.1 REGISTRATION 502
20.3.2 SEGMENTATION 503
20.3.3 RETRIEVAL 506
20.4 EXAMPLES OF SUCCESSFUL EVALUATION CAMPAIGNS 508
20.4.1 REGISTRATION 508
20.4.2 SEGMENTATION 509
20.4.3 ANNOTATION, CLASSIFICATION AND DETECTION 511 20.4.4 INFORMATION
RETRIEVAL 512
20.4.5 IMAGE RETRIEVAL 512
20.5 LESSONS LEARNED 517
20.6 CONCLUSIONS 517
REFERENCES 518
21 TOOLKITS AND SOFTWARE FOR DEVELOPING BIOMEDICAL IMAGE PROCESSING AND
ANALYSIS APPLICATIONS IVO WOLF 521
21.1 INTRODUCTION 521
21.2 TOOLKITS 522
IMAGE 13
CONTENTS XXIII
21.2.1 THE NA-MIC KIT 522
21.2.2 INSIGHT SEGMENTATION AND REGISTRATION TOOLKIT 523 21.2.3 THE
VISUALIZATION TOOLKIT 524
21.2.4 OPEN INVENTOR 525
21.2.5 MEDICAL IMAGING INTERACTION TOOLKIT 526
21.2.6 THE IMAGE-GUIDED SURGERY TOOLKIT 527
21.2.7 THE MULTIMOD APPLICATION FRAMEWORK 528
21.2.8 VTKINRIA3D 529
21.2.9 OFFIS DICOM TOOLKIT 529
21.2.10 GRASSROOTS DICOM LIBRARY 530
21.2.11 THE COMMON TOOLKIT 530
21.2.12 SIMULATION OPEN FRAMEWORK ARCHITECTURE 530
21.3 DEVELOPMENT ENVIRONMENTS 531
21.3.1 SCIRUN 532
21.3.2 OPENXIP 532
21.3.3 DEVIDE 533
21.3.4 VISTRAILS 534
21.3.5 LONI PIPELINE 534
21.3.6 MEVISLAB 535
21.3.7 MATLAB 535
21.3.8 INTERACTIVE DATA LANGUAGE 536
21.4 EXTENSIBLE SOFTWARE 537
21.4.1 3D SLICER 537
21.4.2 MITK EXTAPP AND MITK 3M3 538
21.4.3 GRAPHICAL INTERFACE FOR MEDICAL IMAGE ANALYSIS AND SIMULATION 539
21.4.4 OSIRIX 539
21.4.5 PARAVIEW 539
21.4.6 IMAGEJ AND FIJI 540
21.4.7 MIPAV 541
21.4.8 VOLVIEW 541
21.4.9 ANALYZE 541
21.4.10 AMIRA 542
21.5 CONCLUSION AND DISCUSSION 543
REFERENCES 543
22 IMAGE PROCESSING AND THE PERFORMANCE GAP STEVEN C. HORII AND MURRAY
H. LOEW 545
22.1 INTRODUCTION 545
22.2 EXAMPLES OF CLINICALLY USEFUL IMAGE PROCESSING 546
22.2.1 WINDOWING AND IMAGE DISPLAY 546
22.2.2 CONTRAST AND EDGE ENHANCEMEMT 546
22.2.3 NOISE REDUCTION AND COLOR CODING 547
22.2.4 REGISTRATION AND SEGMENTATION 547
22.2.5 IMAGE COMPRESSION AND MANAGEMENT 548
IMAGE 14
XXIV CONTENTS
22.3 WHY ARE THERE GAPS? 549
22.3.1 THE CONSERVATIVE RADIOLOGIST 549
22.3.2 THE BUSY RADIOLOGIST: DIGITAL VS. ANALOG WORKFLOW 549 22.3.3 THE
WARY RADIOLOGIST: MALPRACTICE CONCERNS 550
22.3.4 THE SKEPTICAL RADIOLOGIST: EVIDENCE-BASED REQUIREMENTS 551
22.3.5 TAILS, DOGS, AND GAPS 552
22.4 THE GOALS OF IMAGE PROCESSING FOR MEDICAL IMAGING 553
22.4.1 AUTOMATION OF TASKS 553
22.4.2 IMPROVEMENT OF OBSERVER PERFORMANCE 555
22.5 CLOSING THE GAP 561
22.5.1 EDUCATION 561
22.5.2 RESEARCH 562
22.6 CONCLUSION 563
REFERENCES 563
INDEX 567
|
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| id | DE-604.BV039843365 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:12:30Z |
| institution | BVB |
| isbn | 9783642158155 |
| language | English |
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| physical | XXXVII, 595 Seiten Illustrationen, Diagramme 25 cm |
| publishDate | 2011 |
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| series2 | Biological and medical physics, biomedical engineering |
| spelling | Biomedical image processing Thomas M. Deserno, editor Berlin Springer [2011] XXXVII, 595 Seiten Illustrationen, Diagramme 25 cm txt rdacontent n rdamedia nc rdacarrier Biological and medical physics, biomedical engineering Literaturangaben Bildgebendes Verfahren (DE-588)4006617-4 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Bildgebendes Verfahren (DE-588)4006617-4 s DE-604 Deserno, Thomas M. 1966- (DE-588)140954120 edt Erscheint auch als Online-Ausgabe, eBook 978-3-642-15816-2 DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024703235&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Biomedical image processing Bildgebendes Verfahren (DE-588)4006617-4 gnd |
| subject_GND | (DE-588)4006617-4 (DE-588)4143413-4 |
| title | Biomedical image processing |
| title_auth | Biomedical image processing |
| title_exact_search | Biomedical image processing |
| title_full | Biomedical image processing Thomas M. Deserno, editor |
| title_fullStr | Biomedical image processing Thomas M. Deserno, editor |
| title_full_unstemmed | Biomedical image processing Thomas M. Deserno, editor |
| title_short | Biomedical image processing |
| title_sort | biomedical image processing |
| topic | Bildgebendes Verfahren (DE-588)4006617-4 gnd |
| topic_facet | Bildgebendes Verfahren Aufsatzsammlung |
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