Compression of Biomedical Images and Signals

Herausgeber: Nait-Ali, Amine; Cavaro-Menard, Christine

• Gebundenes Buch

Produktbeschreibung

During the last decade, image and signal compression for storage and transmission purpose has seen a great expansion. But what about medical data compression? Should a medical image or a physiological signal be processed and compressed like any other data? The progress made in imaging systems, storing systems and telemedicine makes compression in this field particularly interesting. However, this compression has to be adapted to the specificities of biomedical data which contain diagnosis information. As such, this book offers an overview of compression techniques applied to medical data, including: physiological signals, MRI, X-ray, ultrasound images, static and dynamic volumetric images. Researchers, clinicians, engineers and professionals in this area, along with postgraduate students in the signal and image processing field, will find this book to be of great interest.

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Produktdetails

• Verlag: Wiley
• Seitenzahl: 288
• Erscheinungstermin: 1. März 2008
• Englisch
• Abmessung: 234mm x 163mm x 23mm
• Gewicht: 612g
• ISBN-13: 9781848210288
• ISBN-10: 1848210280
• Artikelnr.: 23434860

Autorenporträt

Dr. Amine Naït-Ali is an Associate Professor at the University Paris 12 (France), and a member of the Laboratory LISSI. His research interests are focused on physiological signal processing and analysis, optimisation using metaheuristics, none linear system modeling, biosignal and medical image compression. Dr. Christine Cavaro-Ménard is an Associate Professor at the University of Angers (France), and a member of the Laboratory LISA. Her research interests include medical image processing (segmentation, classification, texture analysis, registration) for diagnosis assistance, medical image compression and quality evaluation of compressed medical images.

Inhaltsangabe

Preface xiii
Chapter 1. Relevance of Biomedical Data Compression 1
Jean-Yves TANGUY, Pierre JALLET, Christel LE BOZEC and Guy FRIJA
1.1. Introduction 1
1.2. The management of digital data using PACS 2
1.2.1. Usefulness of PACS 2
1.2.2. The limitations of installing a PACS 3
1.3. The increasing quantities of digital data 4
1.3.1. An example from radiology 4
1.3.2. An example from anatomic pathology 6
1.3.3. An example from cardiology with ECG 7
1.3.4. Increases in the number of explorative examinations 8
1.4. Legal and practical matters 8
1.5. The role of data compression. 9
1.6. Diagnostic quality 10
1.6.1. Evaluation 10
1.6.2. Reticence 11
1.7. Conclusion 12
1.8. Bibliography 12
Chapter 2. State of the Art of Compression Methods 15
Atilla BASKURT
2.1. Introduction 15
2.2. Outline of a generic compression technique 16
2.2.1. Reducing redundancy 17
2.2.2. Quantizing the decorrelated information 18
2.2.3. Coding the quantized values 18
2.2.4. Compression ratio, quality evaluation 20
2.3. Compression of still images 21
2.3.1. JPEG standard 22
2.3.1.1. Why use DCT? 22
2.3.1.2. Quantization 24
2.3.1.3. Coding 24
2.3.1.4. Compression of still color images with JPEG 25
2.3.1.5. JPEG standard: conclusion 26
2.3.2. JPEG 2000 standard 27
2.3.2.1. Wavelet transform 27
2.3.2.2. Decomposition of images with the wavelet transform 27
2.3.2.3. Quantization and coding of subbands 29
2.3.2.4. Wavelet-based compression methods, serving as references 30
2.3.2.5. JPEG 2000 standard 31
2.4. The compression of image sequences 33
2.4.1. DCT-based video compression scheme 34
2.4.2. A history of and comparison between video standards 36
2.4.3. Recent developments in video compression 38
2.5. Compressing 1D signals 38
2.6. The compression of 3D objects 39
2.7. Conclusion and future developments 39
2.8. Bibliography 40
Chapter 3. Specificities of Physiological Signals and Medical Images 43
Christine CAVARO-MÉNARD, Amine NAÏT-ALI, Jean-Yves TANGUY, Elsa ANGELINI,
Christel LE BOZEC and Jean-Jacques LE JEUNE
3.1. Introduction 43
3.2. Characteristics of physiological signals 44
3.2.1. Main physiological signals 44
3.2.1.1. Electroencephalogram (EEG) 44
3.2.1.2. Evoked potential (EP) 45
3.2.1.3. Electromyogram (EMG) 45
3.2.1.4. Electrocardiogram (ECG) 46
3.2.2. Physiological signal acquisition 46
3.2.3. Properties of physiological signals 46
3.2.3.1. Properties of EEG signals 46
3.2.3.2. Properties of ECG signals 48
3.3. Specificities of medical images 50
3.3.1. The different features of medical imaging formation processes 50
3.3.1.1. Radiology 51
3.3.1.2. Magnetic resonance imaging (MRI) 54
3.3.1.3. Ultrasound 58
3.3.1.4. Nuclear medicine 62
3.3.1.5. Anatomopathological imaging 66
3.3.1.6. Conclusion 68
3.3.2. Properties of medical images 69
3.3.2.1. The size of images 70
3.3.2.2. Spatial and temporal resolution 71
3.3.2.3. Noise in medical images 72
3.4. Conclusion 73
3.5. Bibliography 74
Chapter 4. Standards in Medical Image Compression 77
Bernard GIBAUD and Joël CHABRIAIS
4.1. Introduction 77
4.2. Standards for communicating medical data 79
4.2.1. Who creates the standards, and how? 79
4.2.2. Standards in the healthcare sector 80
4.2.2.1. Technical committee 251 of CEN 80
4.2.2.2. Technical committee 215 of the ISO 80
4.2.2.3. DICOM Committee 80
4.2.2.4.Health Level Seven (HL7) 85
4.2.2.5. Synergy between the standards bodies 86
4.3. Existing standards for image compression 87
4.3.1. Image compression 87
4.3.2. Image compression in the DICOM standard 89
4.3.2.1. The coding of compressed images in DICOM 89
4.3.2.2. The types of compression available 92
4.3.2.3. Modes of access to compressed data 95
4.4. Conclusion 99
4.5. Bibliography 99
Chapter 5. Quality Assessment of Lossy Compressed Medical Images 101
Christine CAVARO-MÉNARD, Patrick LE CALLET, Dominique BARBA and Jean-Yves
TANGUY
5.1. Introduction 101
5.2. Degradations generated by compression norms and their consequences in
medical imaging 102
5.2.1. The block effect 102
5.2.2. Fading contrast in high spatial frequencies 103
5.3. Subjective quality assessment 105
5.3.1. Protocol evaluation 105
5.3.2. Analyzing the diagnosis reliability 106
5.3.2.1. ROC analysis 108
5.3.2.2. Analyses that are not based on the ROC method 111
5.3.3. Analyzing the quality of diagnostic criteria 111
5.3.4. Conclusion 114
5.4. Objective quality assessment 114
5.4.1. Simple signal-based metrics 115
5.4.2. Metrics based on texture analysis 115
5.4.3. Metrics based on a model version of the HVS 117
5.4.3.1. Luminance adaptation 117
5.4.3.2. Contrast sensivity 118
5.4.3.3. Spatio-frequency decomposition 118
5.4.3.4. Masking effect 119
5.4.3.5. Visual distortion measures 120
5.4.4. Analysis of the modification of quantitative clinical parameters 123
5.5. Conclusion 125
5.6. Bibliography 125
Chapter 6. Compression of Physiological Signals 129
Amine NAÏT-ALI
6.1. Introduction 129
6.2. Standards for coding physiological signals 130
6.2.1. CEN/ENV 1064 Norm 130
6.2.2. ASTM 1467 Norm 130
6.2.3. EDF norm 130
6.2.4. Other norms 131
6.3. EEG compression 131
6.3.1. Time-domain EEG compression 131
6.3.2. Frequency-domain EEG compression 132
6.3.3. Time-frequency EEG compression 132
6.3.4. Spatio-temporal compression of the EEG 132
6.3.5. Compression of the EEG by parameter extraction 132
6.4. ECG compression 133
6.4.1. State of the art 133
6.4.2. Evaluation of the performances of ECG compression methods 134
6.4.3. ECG pre-processing 135
6.4.4. ECG compression for real-time transmission 136
6.4.4.1. Time domain ECG compression 136
6.4.4.2. Compression of the ECG in the frequency domain 141
6.4.5. ECG compression for storage 144
6.4.5.1. Synchronization and polynomial modeling 145
6.4.5.2. Synchronization and interleaving 149
6.4.5.3. Compression of the ECG signal using the JPEG 2000 standard 150
6.5. Conclusion 150
6.6. Bibliography 151
Chapter 7. Compression of 2D Biomedical Images 155
Christine CAVARO-MÉNARD, Amine NAÏT-ALI, Olivier DEFORGES and Marie BABEL
7.1. Introduction 155
7.2. Reversible compression of medical images 156
7.2.1. Lossless compression by standard methods 156
7.2.2. Specific methods of lossless compression 157
7.2.3. Compression based on the region of interest 158
7.2.4. Conclusion 160
7.3. Lossy compression of medical images 160
7.3.1. Quantization of medical images 160
7.3.1.1. Principles of vector quantization 161
7.3.1.2. A few illustrations 161
7.3.1.3. Balanced tree-structured vector quantization 163
7.3.1.4. Pruned tree-structured vector quantization 163
7.3.1.5. Other vector quantization methods applied to medical images 163
7.3.2. DCT-based compression of medical images 164
7.3.3. JPEG 2000 lossy compression of medical images 167
7.3.3.1. Optimizing the JPEG 2000 parameters for the compression of medical
images 167
7.3.4. Fractal compression 170
7.3.5. Some specific compression methods 171
7.3.5.1. Compression of mammography images 171
7.3.5.2. Compression of ultrasound images 172
7.4. Progressive compression of medical images 173
7.4.1. State-of-the-art progressive medical image compression techniques
173
7.4.2. LAR progressive compression of medical images 174
7.4.2.1. Characteristics of the LAR encoding method 174
7.4.2.2. Progressive LAR encoding 176
7.4.2.3. Hierarchical region encoding 178
7.5. Conclusion 181
7.6. Bibliography 182
Chapter 8. Compression of Dynamic and Volumetric Medical Sequences 187
Azza OULED ZAID, Christian OLIVIER and Amine NAÏT-ALI
8.1. Introduction 187
8.2. Reversible compression of (2D+t) and 3D medical data sets 190
8.3. Irreversible compression of (2D+t) medical sequences 192
8.3.1. Intra-frame lossy coding 192
8.3.2. Inter-frame lossy coding 194
8.3.2.1. Conventional video coding techniques 194
8.3.2.2. Modified video coders 195
8.3.2.3. 2D+t wavelet-based coding systems limits 195
8.4. Irreversible compression of volumetric medical data sets 196
8.4.1. Wavelet-based intra coding 196
8.4.2. Extension of 2D transform-based coders to 3D data 197
8.4.2.1. 3D DCT coding 197
8.4.2.2. 3D wavelet-based coding based on scalar or vector quantization 198
8.4.2.3. Embedded 3D wavelet-based coding 199
8.4.2.4. Object-based 3D embedded coding 204
8.4.2.5. Performance assessment of 3D embedded coders 205
8.5. Conclusion 207
8.6. Bibliography 208
Chapter 9. Compression of Static and Dynamic 3D Surface Meshes 211
Khaled MAMOU, Françoise PRÊTEUX, Rémy PROST and Sébastien VALETTE
9.1. Introduction 211
9.2. Definitions and properties of triangular meshes 213
9.3. Compression of static meshes 216
9.3.1. Single resolution mesh compression 217
9.3.1.1. Connectivity coding 217
9.3.1.2. Geometry coding 218
9.3.2. Multi-resolution compression 219
9.3.2.1. Mesh simplification methods 219
9.3.2.2. Spectral methods 219
9.3.2.3. Wavelet-based approaches 220
9.4. Compression of dynamic meshes 229
9.4.1. State of the art 230
9.4.1.1. Prediction-based techniques 230
9.4.1.2. Wavelet-based techniques 231
9.4.1.3. Clustering-based techniques 233
9.4.1.4. PCA-based techniques 234
9.4.1.5. Discussion 234
9.4.2. Application to dynamic 3D pulmonary data in computed tomography 236
9.4.2.1. Data 236
9.4.2.2. Proposed approach 237
9.4.2.3. Results 238
9.5. Conclusion 239
9.6. Appendices 240
9.6.1. Appendix A: mesh via the MC algorithm 240
9.7. Bibliography 241
Chapter 10. Hybrid Coding: Encryption-Watermarking-Compression for Medical
Information Security 247
William PUECH and Gouenou COATRIEUX
10.1. Introduction 247
10.2. Protection of medical imagery and data 248
10.2.1. Legislation and patient rights 248
10.2.2. A wide range of protection measures 249
10.3. Basics of encryption algorithms 251
10.3.1. Encryption algorithm classification 251
10.3.2. The DES encryption algorithm 252
10.3.3. The AES encryption algorithm 253
10.3.4. Asymmetric block system: RSA 254
10.3.5. Algorithms for stream ciphering 255
10.4. Medical image encryption 257
10.4.1. Image block encryption 258
10.4.2. Coding images by asynchronous stream cipher 258
10.4.3. Applying encryption to medical images 259
10.4.4. Selective encryption of medical images 261
10.5. Medical image watermarking and encryption 265
10.5.1. Image watermarking and health uses 265
10.5.2. Watermarking techniques and medical imagery 266
10.5.2.1. Characteristics. 266
10.5.2.2. The methods 267
10.5.3. Confidentiality and integrity of medical images by data encryption
and data hiding 269
10.6. Conclusion. 272
10.7. Bibliography 273
Chapter 11. Transmission of Compressed Medical Data on Fixed and Mobile
Networks 277
Christian OLIVIER, Benoît PARREIN and Rodolphe VAUZELLE
11.1. Introduction 277
11.2. Brief overview of the existing applications 278
11.3. The fixed and mobile networks 279
11.3.1. The network principles 279
11.3.1.1. Presentation, definitions and characteristics 279
11.3.1.2. The different structures and protocols 281
11.3.1.3. Improving the Quality of Service 281
11.3.2. Wireless communication systems 282
11.3.2.1. Presentation of these systems 282
11.3.2.2. Wireless specificities 284
11.4. Transmission of medical images 287
11.4.1. Contexts 287
11.4.1.1. Transmission inside a hospital 287
11.4.1.2. Transmission outside hospital on fixed networks 287
11.4.1.3. Transmission outside hospital on mobile networks 288
11.4.2. Encountered problems 288
11.4.2.1. Inside fixed networks 288
11.4.2.2. Inside mobile networks 289
11.4.3. Presentation of some solutions and directions 293
11.4.3.1. Use of error correcting codes 294
11.4.3.2. Unequal protection using the Mojette transform 297
11.5. Conclusion 299
11.6. Bibliography 300
Conclusion 303
List of Authors 305
Index 309