Wavelets in medicine and biology:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
CRC Press
1996
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | 616 S. Ill., graph. Darst. |
| ISBN: | 084939483X |
Internformat
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| 245 | 1 | 0 | |a Wavelets in medicine and biology |c ed. by Akram Aldroubi ... |
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 1996 | |
| 300 | |a 616 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
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| 650 | 2 | |a Algorithme | |
| 650 | 7 | |a Biologie - Mathématiques |2 ram | |
| 650 | 7 | |a IRM |2 inriac | |
| 650 | 7 | |a Imagerie pour le diagnostic |2 ram | |
| 650 | 7 | |a Ondelettes |2 ram | |
| 650 | 2 | |a Technique d'imagerie par résonance magnétique nucléaire | |
| 650 | 7 | |a Traitement du signal |2 ram | |
| 650 | 2 | |a Traitement signal | |
| 650 | 7 | |a analyse statistique |2 inriac | |
| 650 | 7 | |a biologie |2 inriac | |
| 650 | 7 | |a décomposition en ondelettes |2 inriac | |
| 650 | 7 | |a imagerie médicale |2 inriac | |
| 650 | 7 | |a mammographie |2 inriac | |
| 650 | 7 | |a modèle mathématique |2 inriac | |
| 650 | 7 | |a méthode temps-fréquence |2 inriac | |
| 650 | 7 | |a ondelette |2 inriac | |
| 650 | 7 | |a paquet ondelette |2 inriac | |
| 650 | 7 | |a processus ponctuel |2 inriac | |
| 650 | 7 | |a reconnaissance forme |2 inriac | |
| 650 | 7 | |a réduction bruit |2 inriac | |
| 650 | 7 | |a réseau neuronal |2 inriac | |
| 650 | 7 | |a tomographie |2 inriac | |
| 650 | 7 | |a traitement signal |2 inriac | |
| 650 | 7 | |a transformation Radon |2 inriac | |
| 650 | 7 | |a transformée Fourier |2 inriac | |
| 650 | 7 | |a transformée ondelette |2 inriac | |
| 650 | 7 | |a électrocardiogramme |2 inriac | |
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Datensatz im Suchindex
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| adam_text | IMAGE 1
IN JVLEDICINE
A ND B I O L O GY
EDITED BY AKRAM ALDROUBI AND MICHAEL UNSER
CRC PRESS
BOCA RATON NEW YORK LONDON TOKYO
IMAGE 2
CONTENTS
PART I WAVELET TRANSFORM: THEORY AND IMPLEMENTATION
1 THE WAVELET TRANSFORM: A SURFING GUIDE
AKRAM ALDROUBI 1.1 INTRODUCTION 3
1.2 NOTATIONS 8
1.3 THE CONTINUOUS WAVELET TRANSFORM 11
1.3.1 THE CONTINUOUS WAVELET TRANSFORM OF 1-D SIGNALS 11 1.3.2
MULTIDIMENSIONAL WAVELET TRANSFORM 13
1.4 THE DISCRETE WAVELET TRANSFORMS 15
1.4.1 THE DYADIC WAVELET TRANSFORM 15
1.4.2 THE REDUNDANT DISCRETE WAVELET TRANSFORMS . .. 17
1.5 MULTIRESOLUTIONS AND WAVELETS 18
1.5.1 MULTIRESOLUTION APPROXIMATIONS OF L^ 18
1.5.2 ORTHOGONAL MRA-TYPE WAVELETS 20
1.5.3 SEMI-ORTHOGONAL MRA-TYPE WAVELET BASES . . .. 21
1.5.4 BI-ORTHOGONAL MRA-TYPE WAVELET BASES 23
1.5.5 LOCAL AND GLOBAL CHARACTERIZATION OF FUNCTIONS IN TERMS OF THEIR
WAVELET COEFFLCIENTS 23
1.6 SPECIAL BASES OF SCALING FUNCTIONS 24
1.6.1 INTERPOLATING SCALING FUNCTIONS 25
1.6.2 INTERPOLATING WAVELETS 26
1.7 APPLICATIONS AND GENERALIZATIONS 27
1.7.1 APPLICATIONS OF THE WAVELET TRANSFORM 27
1.7.2 GENERALIZATIONS OF THE WAVELET TRANSFORM 28
1.8 FRAME REPRESENTATIONS 28
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IMAGE 3
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2 A PRACTICAL GUIDE TO THE IMPLEMENTATION OF THE WAVELET TRANSFORM
MICHAEL UNSER 2.1 INTRODUCTION 37
2.2 BASIC TOOLS 39
2.2.1 SCALING FUNCTIONS AND MULTIRESOLUTION REPRESENTATIONS 39
2.2.2 INNER PRODUCTS VIA DISCRETE CONVOLUTIONS 42
2.2.3 BOUNDARY CONDITIONS 43
2.3 WAVELET BASES (NONREDUNDANT TRANSFORM) 46
2.3.1 FAST DYADIC WAVELET TRANSFORM 46
2.3.2 IMPLEMENTATION DETAILS 48
2.3.3 EXTENSIONS 52
2.4 DYADIC WAVELET FRAMES 52
2.5 NONDYADIC WAVELET ANALYSES 57
2.5.1 WAVELET REPRESENTATION 58
2.5.2 FAST REDUNDANT DYADIC WAVELET TRANSFORM . . .. 60
2.5.3 FAST REDUNDANT WAVELET TRANSFORM WITH INTEGER SCALES 61
2.5.4 FAST REDUNDANT WAVELET TRANSFORM (ARBITRARY SCALES) 62
2.5.5 FAST REDUNDANT MORLET OR GABOR WAVELET TRANSFORM 65
2.6 CONCLUSION 67
P A RT II WAVELETS IN MEDICAL IMAGING AND TOMOGRAPHY
3 AN APPLICATION OF WAVELET SHRINKAGE TO TOMOGRAPHY ERIC D. KOLACZYK 3.1
INTRODUCTION 77
3.1.1 TOMOGRAPHY 77
3.1.2 WHY WAVELETS? 78
3.1.3 WAVELET SHRINKAGE AND THE PROPOSED METHOD . .. 79
3.2 INVERSION 80
3.2.1 DIRECT DATA VS. INDIRECT DATA 80
3.2.2 THE WAVELET-VAGUELETTE DECOMPOSITION 81
3.2.3 EFFICIENT EXPRESSIONS FOR THE RADON VAGUELETTE COEFFICIENTS 82
3.2.4 CALCULATION OF THE RADON VAGUELETTE COEFHCIENT . . 83 3.3
DENOISING USING WAVELET SHRINKAGE 84
3.3.1 WAVELET SHRINKAGE WITH DIRECT DATA 85
3.3.2 WAVELET SHRINKAGE WITH TOMOGRAPHIE DATA 85
IMAGE 4
3.3.3 THE PROPOSED RECONSTRUCTION METHOD 86
3.4 A SHORT COMPARATIVE STUDY 87
3.5 DISCUSSION 89
4 WAVELET DENOISING OF FUNCTIONAL MRI DATA
MICHAEL HILTON, TODD OGDEN, DAVID HATTERY, GUINEVERE EDEN, AND BJOERN
JAWERTH 4.1 FUNCTIONAL MRI AND BRAIN MAPPING 93
4.2 IMAGE ACQUISITION 95
4.3 FMRI TIME SERIES ANALYSIS 96
4.3.1 THE HEMODYNAMIC RESPONSE FUNCTION 97
4.4 WAVELET DENOISING OF SIGNALS 98
4.4.1 DATA ANALYTIC THRESHOLDING 100
4.5 EXPERIMENTAL RESULTS 104
4.5.1 DATA SET DESCRIPTIONS 104
4.5.2 ANALYSIS TECHNIQUE 105
4.5.3 DENOISING RESULTS 108
4.6 CONCLUSIONS 111
4.7 ACKNOWLEDGMENT 112
5 STATISTICAL ANALYSIS OF IMAGE DIFFERENCES BY WAVELET DECOMPOSITION
URS E. RUTTIMANN, MICHAEL UNSER, PHILIPPE THEVENAZ, CHULHEE LEE, DANIEL
RIO, AND DANIEL W. HOMMER 5.1 INTRODUCTION 115
5.2 WAVELET TRANSFORM 119
5.3 CORRELATION OF WAVELET COEFFICIENTS 123
5.4 STATISTICAL TESTS 128
5.5 EXPERIMENTAL RESULTS 132
5.5.1 FUNCTIONAL MAGNETIC RESONANCE IMAGES 132
5.5.2 POSITRON EMISSION TOMOGRAPHY IMAGES 137
5.6 DISCUSSION 139
6 FEATURE EXTRACTION IN DIGITAL MAMMOGRAPHY
R. A. DEVORE, B. LUCIER, AND Z. YANG 6.1 INTRODUCTION 145
6.2 MAMMOGRAMS AS DIGITIZED IMAGES 146
6.2.1 CHARACTERISTICS OF MAMMOGRAPHIE IMAGES 148
6.3 COMPRESSION AND NOISE REMOVAL 149
6.4 SOME ISSUES IN COMPRESSION ALGORITHMS 152
6.4.1 CHOICE OF WAVELET BASIS 152
6.4.2 CHOICE OF METRIE 153
6.4.3 LEVEL OF COMPRESSION 153
IMAGE 5
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6.5 ALGORITHMS 153
6.6 EXAMPLES 156
7 MULTISCALE CONTRAST ENHANCEMENT AND DENOISING IN DIGITAL RADIOGRAPHS
JIAN FAN AND ANDREW LAINE 7.1 INTRODUCTION 163
7.2 ONE-DIMENSIONAL WAVELET TRANSFORM 165
7.2.1 GENERAL STRUCTURE AND CHANNEL CHARACTERISTICS . . 165 7.2.2 TWO
POSSIBLE FILTERS 168
7.3 LINEAR ENHANCEMENT AND UNSHARP MASKING 170
7.3.1 REVIEW OF UNSHARP MASKING 170
7.3.2 INCLUSION OF UNSHARP MASKING WITHIN RDWT FRAMEWORK 171
7.4 NONLINEAR ENHANCEMENT 173
7.4.1 MINIMUM CONSTRAINT FOR AN ENHANCEMENT FUNCTION 173
7.4.2 FILTER SELECTION 173
7.4.3 A NONLINEAR ENHANCEMENT FUNCTION 174
7.5 COMBINED DENOISING AND ENHANCEMENT 176
7.5.1 INCORPORATING WAVELET SHRINKAGE INTO ENHANCEMENT 177
7.5.2 THRESHOLD ESTIMATION FOR DENOISING 179
7.6 TWO-DIMENSIONAL EXTENSION T 180
7.7 EXPERIMENTAL RESULTS AND COMPARISONS 180
7.8 CONCLUSION 183
7.9 ACKNOWLEDGMENT 187
8 USING WAVELETS TO SUPPRESS NOISE IN BIOMEDICAL IMAGES
MAURITS MALFAIT 8.1 INTRODUCTION 192
8.2 OVERVIEW OF WAVELET-BASED NOISE SUPPRESSION 193
8.2.1 WAVELET SHRINKAGE 193
8.2.2 CORRELATING COEFFICIENTS BETWEEN WAVELET LEVELS . . 194 8.2.3
SMOOTHNESS MEASURE FROM WAVELET EXTREMA . . .. 195
8.2.4 EXAMPLE 195
8.3 INTRODUCING AN A PRIORI MODEL 196
8.3.1 MOTIVATION 196
8.3.2 BASIC IDEA AND NOTATION 198
8.3.3 BAYESIAN METHOD 199
8.3.4 THE CONDITIONAL PROBABILITY 200
8.3.5 THE A PRIORI PROBABILITY 201
8.3.6 COEFFICIENT MANIPULATION 201
IMAGE 6
8.4 RESULTS FOR BIOMEDICAL IMAGES
202
9 WAVELET TRANSFORM AND TOMOGRAPHY: CONTINUOUS AND DISCRETE APPROACHES
F. PEYRIN AND M. ZAIM 9.1 INTRODUCTION 210
9.2 BASIS OF TOMOGRAPHY 211
9.2.1 PROBLEM POSITION 211
9.2.2 RECONSTRUCTION METHODS: TRANSFORM METHODS . .. 212 9.2.3 SERIES
EXPANSION METHODS 213
9.3 CONTINUOUS WAVELET DECOMPOSITION 214
9.3.1 CONTINUOUS WAVELET DECOMPOSITION OF PROJECTIONS 214
9.3.2 CONTINUOUS WAVELET DECOMPOSITION OF THE IMAGE 216
9.4 DISCRETE WAVELET DECOMPOSITION 219
9.4.1 1-D DWT OF THE PROJECTIONS 220
9.4.2 2-D DISCRETE WT OF THE IMAGE 223
9.5 CONCLUSION 225
9.5.1 ACKNOWLEDGMENTS 225
9.6 APPENDIX 1 226
10 WAVELETS AND LOCAL TOMOGRAPHY Y
CARLOS A. BERENSTEIN AND DAVID F. WALNUT 10.1 INTRODUCTION 231
10.2 BACKGROUND AND NOTATION 233
10.3 WHY WAVELETS? 235
10.3.1 THE NONLOCALITY OF THE RADON TRANSFORM 235
10.3.2 WAVELETS, VANISHING MOMENTS, AND A-TOMOGRAPHY 236
10.4 WAVELET INVERSION OF THE RADON TRANSFORM 237
10.4.1 THE CONTINUOUS WAVELET TRANSFORM 237
10.4.2 THE SEMI-CONTINUOUS WAVELET TRANSFORM 239
10.4.3 THE DISCRETE WAVELET TRANSFORM 241
10.5 WAVELET LOCALIZATION OF RADON TRANSFORM 249
10.6 CONCLUSIONS 251
10.7 APPENDIX: PROOFS OF THEOREMS 251
10.8 ACKNOWLEDGMENTS 258
11 OPTIMAL TIME-PREQUENCY PROJECTIONS FOR LOCALIZED TOMOGRAPHY
TIM OLSON 11.1 INTRODUCTION 263
K
IMAGE 7
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11.1.1 HISTORICAL NOTES 263
11.1.2 PRIOR WORK 264
11.1.3 ORGANIZATION 266
11.2 ALGORITHMIC GOALS 266
11.3 BACKGROUND 266
11.3.1 THE RADON TRANSFORM 266
11.3.2 BASIC FOURIER ANALYSIS 270
11.4 RECONSTRUCTION TECHNIQUES 271
11.4.1 FOURIER RECONSTRUCTION 271
11.4.2 FILTERED BACKPROJECTION 273
11.4.3 NONLOCALITY OF THE RADON INVERSION 273
11.4.4 VISUALIZATION VIA THE SINOGRAM 276
11.4.5 COMPARISON TO LOCAL TOMOGRAPHY 277
11.5 LOCALIZATION 278
11.5.1 UTILIZING FUNCTIONS WITH ZERO MOMENTS 278
11.5.2 HOW MANY FREQUENCY WINDOWS? 278
11.5.3 HIGH FREQUENCY COMPUTATION 279
11.5.4 LOW FREQUENCY COMPUTATION 280
11.5.5 THE ALGORITHM 281
11.6 NUMERICAL RESULTS 282
11.7 OPTIMALITY 283
11.7.1 MINIMIZATION OF NONLOCAL DATA 287
11.8 CONCLUSION 288
11.9 APPENDIX: ERROR ANALYSIS 289
11.9.1 ALIASING ERROR ANALYSIS 289
11.9.2 TRUNCATION ERROR ANALYSIS 291
11.10 LOCAL COSINE AND SINE BASES 292
11.11 ACKNOWLEDGMENTS 295
12 ADAPTED WAVELET TECHNIQUES FOR ENCODING MAGNETIC RESONANCE IMAGES
DENNIS M. HEALY, JR. AND JOHN B. WEAVER 12.1 INTRODUCTION 298
12.2 ENCODING IN MAGNETIC RESONANCE IMAGING 299
12.2.1 NUCLEAR MAGNETIC RESONANCE 300
12.2.2 IMAGING 303
12.2.3 IMAGING TIME AND SIGNAL-TO-NOISE RATIO 311
12.3 ADAPTED WAVEFORM ENCODING IN MRI 314
12.3.1 MRI ENCODING WITH A BASIS 315
12.3.2 FIGURES OF MERIT IN ADAPTED WAVEFORM ENCODING 324
12.3.3 CHOOSING A BASIS FOR ENCODING 329
12.3.4 IMPLEMENTATION OF ADAPTED WAVEFORM ENCODING . . 330
IMAGE 8
12.4 REDUCED IMAGING TIMES 334
12.4.1 ADAPTED WAVEFORM ENCODING WITH K-L BASES . . . 335 12.4.2
APPROXIMATE K-L BASES 339
12.4.3 APPROXIMATE KARHUNEN-LOEVE ENCODING 342
12.5 CONCLUSIONS 346
12.6 ACKNOWLEDGMENTS 347
P A RT III WAVELETS AND BIOMEDICAL SIGNAL PROCESSING
13 SLEEP IMAGES USING THE WAVELET TRANSFORM TO PROCESS POLYSOMNOGRAPHIE
SIGNALS
RICHARD SARTENE, LAURENT POUPARD, JEAN-LOUIS BERNARD, AND
JEAN-CHRISTOPHE WALLET 13.1 INTRODUCTION 355
13.2 SLEEP POLYGRAPHY 357
13.2.1 SIGNALS 357
13.2.2 SLEEP ARCHITECTURE (FIGURE 13.3) 360
13.2.3 SLEEP AND CARDIORESPIRATORY ACTIVITY [5, 9] 362
13.3 THE WAVELET TRANSFORM-PRACTICAL USE 365
13.3.1 PRACTICAL CONSIDERATIONS 365
13.3.2 VALIDATION OF THE MODULATION LAWS (FM-AM) . .. 367 13.4
APPLICATION OF THE WAVELET TRANSFORM 370
13.5 CARDIORESPIRATORY VARIATIONS 376
13.6 INTERACTION BETWEEN TWO SYSTEMS 377
13.7 CONCLUSION-PERSPECTIVES 380
13.8 ACKNOWLEDGMENTS 381
14 ESTIMATING THE FRACTAL EXPONENT OF POINT PROCESSES IN BIOLOGICAL
SYSTEMS USING WAVELET- AND FOURIER-TRANSFORM METHODS Y
MALVIN C. TEICH, CONOR HENEGHAN, STEVEN B. LOEWEN, AND ROBERT G. TURCOTT
14.1 INTRODUCTION 383
14.1.1 MATHEMATICAL DESCRIPTIONS OF STOCHASTIC POINT PROCESSES 384
14.1.2 FRACTAL STOCHASTIC POINT PROCESSES (FSPPS) EXHIBIT SCALING 385
14.1.3 THE STANDARD FRACTAL RENEWAL PROCESS 386
14.1.4 EXAMPLES OF FRACTAL STOCHASTIC POINT PROCESSES IN NATURE 387
14.2 ESTIMATING THE FRACTAL EXPONENT 388
14.2.1 COINCIDENCE RATE 389
V.
IMAGE 9
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14.2.2 POWER SPECTRAL DENSITY 389
14.2.3 FANO FACTOR 390
14.2.4 ALLAN FACTOR 392
14.2.5 HAAR-BASIS REPRESENTATION OF THE FANO AND ALLAN FACTORS 393
14.2.6 WAVELET-BASED FANO AND ALLAN FACTORS 397
14.3 COMPARISON OF TECHNIQUES 402
14.4 DISCUSSION 405
14.5 CONCLUSION 408
14.6 ACKNOWLEDGMENTS 408
15 POINT PROCESSES, LONG-RANGE DEPENDENCE AND WAVELETS
PATRICE ABRY AND PATRICK FLANDRIN 15.1 MOTIVATION 413
15.1.1 LONG-RANGE DEPENDENCE 413
15.1.2 POINT PROCESSES 414
15.1.3 LONG-RANGE DEPENDENT POINT PROCESSES 415
15.1.4 FANO FACTOR 415
15.1.5 WAVELET ANALYSIS 415
15.2 THE STANDARD FANO FACTOR 416
15.2.1 SOME DEFINITIONS 416
15.2.2 POISSON PROCESS: THEME AND VARIATIONS 416
15.2.3 A LONG-DEPENDENT POISSON PROCESS 417
15.2.4 MAIN LIMITATIONS 418
15.3 THE WAVELET-BASED FANO FACTOR 419
15.3.1 THE MULTIRESOLUTION POINT OF VIEW 419
15.3.2 UNBIASED ESTIMATION OF THE LONG RANGEDEPENDENCE PARAMETER: A KEY
FEATURE 422
15.3.3 REDUCTION OF THE RANGE OF THE DEPENDENCE: ANOTHER KEY FEATURE 424
15.3.4 FANO FACTOR, ALLAN VARIANCE AND WAVELETS 426
15.3.5 CHOOSING THE NUMBER OF VANISHING MOMENTS AF . . 427 15.4
PRACTICAL ISSUES 428
15.5 FANO FACTOR AND SPECTRAL ESTIMATION 430
15.6 AN EXAMPLE: SPIKETRAIN OF AN AUDITORY-NERVE RESPONSE . 432 15.7
CONCLUSION 434
16 CONTINUOUS WAVELET TRANSFORM: ECG RECOGNITION BASED ON PHASE AND
MODULUS REPRESENTATIONS AND HIDDEN MARKOV MODELS
LOTFI SENHADJI, LAURENT THORAVAL, AND GUY CARRAULT 16.1 INTRODUCTION 439
16.2 PROPERTIES OF SQUARE MODULUS AND PHASE 441
IMAGE 10
16.2.1 SQUARE MODULUS APPROXIMATION 441
16.2.2 PHASE BEHAVIOR 442
16.3 ILLUSTRATION ON SIGNALS 445
16.3.1 RESULTS ON SIMULATED DATA 445
16.3.2 RESULTS ON REAL DATA 447
16.4 CARDIAC BEAT RECOGNITION 448
16.5 RESULTS 458
16.6 CONCLUSION 460
16.7 APPENDIX 460
17 INTERFERENCE CANCELING IN BIOMEDICAL SYSTEMS: THE MUTUAL WAVELET
PACKETS APPROACH
MOHSINE KARRAKCHOU AND MURAT KUNT 17.1 INTRODUCTION 465
17.2 PULMONARY CAPILLARY PRESSURE: A SHORT REVIEW 466
17.2.1 CLINICAL RELEVANCE 466
17.2.2 IN VIVO ESTIMATION: THE OCCLUSION TECHNIQUES . . 468 17.2.3
LIMITATIONS IN PATIENTS 471
17.3 BASICS OF INTERFERENCE CANCELING 472
17.3.1 CLASSICAL FIR ADAPTIVE FILTERING 472
17.4 MULTIRATE ADAPTIVE FILTERING 475
17.4.1 FUNDAMENTALS OF ADAPTIVE FILTERING IN SUBBANDS 476
17.5 WAVELET PACKETS 477
17.5.1 THE BEST BASIS METHOD 478
17.6 MUTUAL WAVELET PACKET DECOMPOSITION 480
17.6.1 INTRODUCTORY COMMENTS 480
17.6.2 THE MUTUAL WAVELET PACKETS DECOMPOSITION . . .. 480 17.6.3
IMPLEMENTATION SCHEME 482
17.6.4 ALGORITHMIC COMPLEXITY 482
17.6.5 EXPERIMENTAL RESULTS 484
17.7 CONCLUSION 486
18 FRAME SIGNAL PROCESSING APPLIED TO BIOELECTRIC DATA
JOHN J. BENEDETTO 18.1 INTRODUCTION 493
18.2 NOTATION 494
18.3 THE THEORY OF FRAMES 494
18.3.1 GABOR AND WAVELET SYSTEMS 494
18.3.2 FRAMES _J . . . . 495
18.4 FRAME MULTIRESOLUTION ANALYSIS (FMRA) 496
18.5 NOISE REDUCTION 498
18.6 THE LAPLACIAN METHOD AND GAUSSIAN FRAMES 501
FC
IMAGE 11
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18.7 AN INTERPRETATION OF SPECTRAL ECOG DATA 503
18.8 APPENDIX 508
19 DIAGNOSJS OF CORONARY ARTERY DISEASE USING WAVELET-BASED NEURAL
NETWORKS
METIN AKAY 19.1 INTRODUCTION 513
19.2 METHOD 516
19.2.1 FAST WAVELET TRANSFORM 516
19.2.2 FUZZY MIN-MAX NEURAL NETWORKS 517
19.2.3 PATIENT ANALYSIS 518
19.3 RESULTS 519
19.3.1 FEATURE EXTRACTION 519
19.3.2 NETWORK OUTPUT REPRESENTATION 521
19.4 CONCLUSION 522
19.5 ACKNOWLEDGMENT 522
P A RT IV WAVELETS AND M A T H E M A T I C AL MODELS IN
BIOLOGY
20 A NONLINEAR SQUEEZING OF THE CONTINUOUS WAVELET TRANSFORM BASED ON
AUDITORY NERVE MODELS
INGRID DAUBECHIES AND STEPHANE MAES 20.1 INTRODUCTION 527
20.2 COCHLEAR FILTERING 528
20.3 INFORMATION COMPRESSION 529
20.4 THE MODULATION MODEL FOR SPEECH 531
20.5 SQUEEZING THE CONTINUOUS WAVELET TRANSFORM 533
20.6 SHORT DISCUSSION 538
20.7 RESULTS ON SPEECH SIGNALS 540
20.8 ACKNOWLEDGMENTS 544
21 THE APPLICATION OF WAVELET TRANSFORMS TO BLOOD FLOW VELOCIMETRY
LORA G. WEISS 21.1 INTRODUCTION 547
21.2 1-D MEASUREMENT DEVICES 549
21.3 1-D VELOCIMETRY METHODS 551
21.3.1 DOPPLER METHODS 552
21.3.2 TIME DOMAIN CORRELATION METHODS 554
21.3.3 LIMITATIONS 555
21.3.4 SUMMARY OF DESIRABLE SIGNAL CHARACTERISTICS . . .. 557
IMAGE 12
21.4 WIDEBAND/WAVELET TRANSFORM PROCESSING 557
21.4.1 WAVELET TRANSFORM PROCESSING 557
21.4.2 PARAMETER ESTIMATION 560
21.4.3 EXAMPLE 562
21.5 CONCLUSIONS 565
21.6 ACKNOWLEDGMENT 566
21.7 APPENDIX 567
22 WAVELET MODELS OF EVENT-RELATED POTENTIALS
JONATHAN RAZ AND BRUCE TURETSKY 22.1 INTRODUCTION 571
22.2 THE SINGLE CHANNEL WAVELET MODEL 573
22.3 APPLICATION TO CAT POTENTIALS 575
22.4 THE TOPOGRAPHIE WAVELET MODEL 578
22.5 APPLICATION OF TOPOGRAPHIE WAVELET MODEL 580
22.6 DISCUSSION 585
22.7 ACKNOWLEDGMENTS 586
23 MACROMOLECULAR STRUCTURE COMPUTATION BASED ON ENERGY FUNCTION
APPROXIMATION BY WAVELETS
EBERHARD SCHMITT 23.1 INTRODUCTION 590
23.2 DOMAIN AND FUNCTION DECOMPOSITION 592
23.2.1 REDUCTION OF THE DEGREES OF FREEDOM 592
23.2.2 REPRESENTATION OF THE ENERGY FUNCTION 595
23.3 APPROXIMATION BY WAVELETS 597
23.3.1 LOCAL APPROXIMATION BY CUBIC B-SPLINE WAVELETS 597
23.3.2 GLOBAL APPROXIMATION BY TENSOR PRODUCTS 598
23.4 FURTHER APPLICATIONS: SURFACE REPRESENTATION 602
23.5 DISCUSSION 603
INDEX 607
|
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| building | Verbundindex |
| bvnumber | BV013078282 |
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| ctrlnum | (OCoLC)301596152 (DE-599)BVBBV013078282 |
| discipline | Informatik Medizintechnik Mathematik Medizin |
| format | Book |
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| id | DE-604.BV013078282 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T18:38:43Z |
| institution | BVB |
| isbn | 084939483X |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-008910206 |
| oclc_num | 301596152 |
| open_access_boolean | |
| owner | DE-29T DE-91G DE-BY-TUM DE-188 DE-83 |
| owner_facet | DE-29T DE-91G DE-BY-TUM DE-188 DE-83 |
| physical | 616 S. Ill., graph. Darst. |
| publishDate | 1996 |
| publishDateSearch | 1996 |
| publishDateSort | 1996 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Wavelets in medicine and biology ed. by Akram Aldroubi ... Boca Raton [u.a.] CRC Press 1996 616 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Algorithme Biologie - Mathématiques ram IRM inriac Imagerie pour le diagnostic ram Ondelettes ram Technique d'imagerie par résonance magnétique nucléaire Traitement du signal ram Traitement signal analyse statistique inriac biologie inriac décomposition en ondelettes inriac imagerie médicale inriac mammographie inriac modèle mathématique inriac méthode temps-fréquence inriac ondelette inriac paquet ondelette inriac processus ponctuel inriac reconnaissance forme inriac réduction bruit inriac réseau neuronal inriac tomographie inriac traitement signal inriac transformation Radon inriac transformée Fourier inriac transformée ondelette inriac électrocardiogramme inriac Medizin (DE-588)4038243-6 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 gnd rswk-swf Wavelet (DE-588)4215427-3 gnd rswk-swf Bildverarbeitung (DE-588)4006684-8 s Wavelet (DE-588)4215427-3 s Medizin (DE-588)4038243-6 s DE-604 Aldroubi, Akram Sonstige oth GBV Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008910206&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Wavelets in medicine and biology Algorithme Biologie - Mathématiques ram IRM inriac Imagerie pour le diagnostic ram Ondelettes ram Technique d'imagerie par résonance magnétique nucléaire Traitement du signal ram Traitement signal analyse statistique inriac biologie inriac décomposition en ondelettes inriac imagerie médicale inriac mammographie inriac modèle mathématique inriac méthode temps-fréquence inriac ondelette inriac paquet ondelette inriac processus ponctuel inriac reconnaissance forme inriac réduction bruit inriac réseau neuronal inriac tomographie inriac traitement signal inriac transformation Radon inriac transformée Fourier inriac transformée ondelette inriac électrocardiogramme inriac Medizin (DE-588)4038243-6 gnd Bildverarbeitung (DE-588)4006684-8 gnd Wavelet (DE-588)4215427-3 gnd |
| subject_GND | (DE-588)4038243-6 (DE-588)4006684-8 (DE-588)4215427-3 |
| title | Wavelets in medicine and biology |
| title_auth | Wavelets in medicine and biology |
| title_exact_search | Wavelets in medicine and biology |
| title_full | Wavelets in medicine and biology ed. by Akram Aldroubi ... |
| title_fullStr | Wavelets in medicine and biology ed. by Akram Aldroubi ... |
| title_full_unstemmed | Wavelets in medicine and biology ed. by Akram Aldroubi ... |
| title_short | Wavelets in medicine and biology |
| title_sort | wavelets in medicine and biology |
| topic | Algorithme Biologie - Mathématiques ram IRM inriac Imagerie pour le diagnostic ram Ondelettes ram Technique d'imagerie par résonance magnétique nucléaire Traitement du signal ram Traitement signal analyse statistique inriac biologie inriac décomposition en ondelettes inriac imagerie médicale inriac mammographie inriac modèle mathématique inriac méthode temps-fréquence inriac ondelette inriac paquet ondelette inriac processus ponctuel inriac reconnaissance forme inriac réduction bruit inriac réseau neuronal inriac tomographie inriac traitement signal inriac transformation Radon inriac transformée Fourier inriac transformée ondelette inriac électrocardiogramme inriac Medizin (DE-588)4038243-6 gnd Bildverarbeitung (DE-588)4006684-8 gnd Wavelet (DE-588)4215427-3 gnd |
| topic_facet | Algorithme Biologie - Mathématiques IRM Imagerie pour le diagnostic Ondelettes Technique d'imagerie par résonance magnétique nucléaire Traitement du signal Traitement signal analyse statistique biologie décomposition en ondelettes imagerie médicale mammographie modèle mathématique méthode temps-fréquence ondelette paquet ondelette processus ponctuel reconnaissance forme réduction bruit réseau neuronal tomographie traitement signal transformation Radon transformée Fourier transformée ondelette électrocardiogramme Medizin Bildverarbeitung Wavelet |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008910206&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT aldroubiakram waveletsinmedicineandbiology |