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Advances in Diagnostic and
Therapeutic Ultrasound Imaging
Artech House Series
Bioinformatics & Biomedical Imaging
Series Editors
Stephen T. C. Wong, The Methodist Hospital and Weill Cornell Medical College
Guang-Zhong Yang, Imperial College
Advances in Diagnostic and Therapeutic Ultrasound Imaging, Jasjit S. Suri,
Chirinjeev Kathuria, Ruey-Feng Chang, Filippo Molinari, and Aaron Fenster,
editors
Biological Database Modeling, Jake Chen and Amandeep S. Sidhu, editors
Biomedical Informatics in Translational Research, Hai Hu, Michael Liebman,
and Richard Mural
Genome Sequencing Technology and Algorithms, Sun Kim, Haixu Tang, and
Elaine R. Mardis, editors
Life Science Automation Fundamentals and Applications, Mingjun Zhang,
Bradley Nelson, and Robin Felder, editors
Microscopic Image Analysis for Life Science Applications, Jens Rittscher,
Stephen T. C. Wong, and Raghu Machiraju, editors
Next Generation Artificial Vision Systems: Reverse Engineering the Human
Visual System, Maria Petrou and Anil Bharath, editors
Systems Bioinformatics: An Engineering Case-Based Approach, Gil Alterovitz
and Marco F. Ramoni, editors
Advances in Diagnostic and
Therapeutic Ultrasound Imaging
Jasjit S. Suri
Chirinjeev Kathuria
Ruey-Feng Chang
Filippo Molinari
Aaron Fenster
Editors
artechhouse.com
Library of Congress Cataloging-in-Publication Data
A catalog record for this book is available from the U.S. Library of Congress.
British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library.
ISBN-13: 978-1-59693-144-2
Cover design by Igor Valdman
© 2008 ARTECH HOUSE, INC.
685 Canton Street
Norwood, MA 02062
All rights reserved. Printed and bound in the United States of America. No part of this book
may be reproduced or utilized in any form or by any means, electronic or mechanical, includ-
ing photocopying, recording, or by any information storage and retrieval system, without
permission in writing from the publisher.
All terms mentioned in this book that are known to be trademarks or service marks have
been appropriately capitalized. Artech House cannot attest to the accuracy of this informa-
tion. Use of a term in this book should not be regarded as affecting the validity of any trade-
mark or service mark.
DISCLAIMER OF WARRANTY
The technical descriptions, procedures, and computer programs in this book have been devel-
oped with the greatest of care and they have been useful to the author in a broad range of
applications; however, they are provided as is, witho ut warranty of any kind. Artech House,
Inc. and the author and editors of the book titled Advances in Diagnostic and Therapeutic
Ultrasound Imaging make no warranties, expressed or implied, that the equations, pro-
grams, and procedures in this book or its associated software are free of error, or are consis-
tent with any particular standard of merchantability, or will meet your requirements for any
particular application. They should not be relied upon for solving a problem whose incorrect
solution couldresultininjuryto a person orlossof property. Any use of the programs or pro-
cedures in such a manneris at theuser’s ownrisk. Theeditors, author,and publisher disclaim
all liability for direct, incidental, or consequent damages resulting from use of the programs
or procedures in this book or the associated software.
10 9 8 7 6 5 4 3 2 1
Disclaimer:
This eBook does not include the ancillary media that was
packaged with the original printed version of the book.
Contents
Preface xv
PART I
Recent Advances in Ultrasound Instrumentation 1
CHAPTER 1
3-D Ultrasound Imaging 3
1.1 Introduction 3
1.2 Disadvantages of 2-D Ultrasound 4
1.3 3-D Ultrasound Scanning Techniques 4
1.4 Mechanical Scanning 5
1.4.1 Linear Mechanical 3-D Scanners 6
1.4.2 Tilt 3-D Mechanical Scanners 7
1.4.3 Endocavity Rotational 3-D Scanners 7
1.5 Free-Hand Scanning with Position Sensing 9
1.5.1 Tracked 3-D US with Articulated Arm Sensing 9
1.5.2 Free-Hand 3-D US with Acoustic Tracking 9
1.5.3 Free-Hand 3-D Scanning with Magnetic Field Sensing 10
1.5.4 3-D US Tracked by Speckle Decorrelation 10
1.6 Free-Hand 3-D US Scanning Without Position Sensing 11
1.7 2-D Array Scanning for Dynamic 3-D Ultrasound (4-D US) 11
1.8 3-D Ultrasound Visualization 11
1.8.1 Multiplanar Reformatting 11
1.8.2 Volume Rendering Techniques 12
1.9 3-D Ultrasound-Guided Prostate Therapy 13
1.9.1 Early-Stage Prostate Cancer Management 14
1.9.2 US-Guided Prostate Brachytherapy 15
1.9.3 3-D TRUS-Guided Brachytherapy System 15
1.9.4 3-D TRUS Imaging System 16
1.9.5 Prostate Segmentation 17
1.9.6 Dosimetry 19
1.9.7 Calibration of the Coordinate Systems 20
1.9.8 Needle Segmentation 22
v
1.10 Evaluation of 3-D TRUS-Guided Brachytherapy System 22
1.10.1 Methods of Calibration 23
1.10.2 Results of Calibration 24
1.10.3 Accuracy of Needle Placement 25
1.10.4 Accuracy of Needle Angulation 26
1.10.5 Accuracy of Needle Targeting 26
1.10.6 Accuracy of Needle Tracking 26
1.10.7 Accuracy of Seed Implantation 28
1.11 Conclusions 28
Acknowledgments 29
References 30
CHAPTER 2
Despeckle Filtering in Ultrasound Imaging of the Carotid Artery 37
2.1 Introduction 37
2.2 Despeckle Filtering 40
2.2.1 Local Statistics Filtering 41
2.2.2 Median Filtering (median)42
2.2.3 Maximum Homogeneity over a Pixel Neighborhood Filtering
(homog)42
2.2.4 Geometric Filtering (gf4d)43
2.2.5 Homomorphic Filtering (homo)44
2.2.6 Diffusion Filtering 44
2.2.7 Wavelet Filtering (waveltc)46
2.3 Methodology 47
2.3.1 Material 47
2.3.2 Recording of Ultrasound Images 47
2.3.3 Despeckle Filtering 47
2.3.4 Texture Analysis 48
2.3.5 Distance Measures 49
2.3.6 Univariate Statistical Analysis 50
2.3.7 kNN Classifier 50
2.3.8 Image Quality Evaluation Metrics 50
2.3.9 Visual Evaluation by Experts 50
2.4 Results 51
2.4.1 Evaluation of Despeckle Filtering on a Symptomatic Ultrasound
Image 51
2.4.2 Texture Analysis: Distance Measures 51
2.4.3 Texture Analysis: Univariate Statistical Analysis 54
2.4.4 Texture Analysis: kNN Classifier 54
2.4.5 Image Quality Evaluation Metrics 54
2.4.6 Visual Evaluation by Experts 56
2.5 Discussion 57
Acknowledgments 60
References 60
vi Contents
PART II
2-D/3-D Ultrasound in Vascular Imaging 65
CHAPTER 3
3-D US Imaging of the Carotid Arteries 67
3.1 Introduction 67
3.1.1 Limitations of Conventional Carotid Ultrasound Imaging 68
3.2 3-D US Imaging of the Carotid Arteries 69
3.2.1 3-D Carotid Ultrasound Scanning Techniques 69
3.2.2 Reconstruction of 3-D US Images 72
3.2.3 Viewing of 3-D US Images 73
3.3 Measurement of Plaque Volume 73
3.3.1 Plaque Segmentation by Manual Planimetry 73
3.3.2 Studies of Plaque Volume 74
3.3.3 The Effects of Interslice Distance 77
3.3.4 The Accuracy of Plaque Volume Measurements 77
3.3.5 Variability of Plaque Volume Measurements 78
3.3.6 Automated Segmentation of Plaques from 3-D US Images 79
3.3.7 Plaque Surface Morphology 82
3.3.8 Local Distribution and Change of Carotid Wall and Plaque 83
3.3.9 Example Results 86
3.4 Trends and Future Developments 87
3.4.1 The Need for Improved Free-Hand Scanning 88
3.4.2 The Need for Improved Visualization Tools 88
3.4.3 Reduction in the Variability of Plaque Volume Measurement 88
3.4.4 ECG-Triggered Acquisition of 3-D Carotid US Images 88
Acknowledgments 89
References 89
CHAPTER 4
Quality Evaluation of Ultrasound Imaging of the Carotid Artery 93
4.1 Introduction 93
4.2 Methodology 94
4.2.1 Ultrasound Imaging Scanners 94
4.2.2 Material 95
4.2.3 Speckle Reduction 96
4.2.4 Image Normalization 96
4.2.5 Statistical and Texture Analysis 97
4.3 Image Quality and Evaluation Metrics 98
4.3.1 Normalized Mean-Square-Error 98
4.3.2 Normalized Root Mean-Square-Error 98
4.3.3 Normalized Error Summation in the Form of the Minkowski
Metric 98
4.3.4 Normalized Geometric Average Error 99
4.3.5 Normalized Signal-to-Noise Ratio 99
4.3.6 Normalized Peak Signal-to-Noise Radio 99
4.3.7 Mathematically Defined Universal Quality Index 100
Contents vii
4.3.8 Structural Similarity Index 100
4.4 Visual Perception Evaluation 100
4.5 Results 101
4.6 Discussion 105
Acknowledgments 108
References 108
CHAPTER 5
User-Independent Plaque Segmentation and Accurate Intima-Media Thickness
Measurement of Carotid Artery Wall Using Ultrasound 111
5.1 Ultrasound Imaging of the Carotid Wall 112
5.1.1 Carotid Artery Anatomy and Pathology 112
5.1.2 Principles and Validity of Ultrasound Analysis of the Carotid
Artery 113
5.2 Algorithms for the Segmentation of Carotid Artery Ultrasound Images 116
5.2.1 Different Approaches for ROI Extraction 116
5.2.2 Approaches to Image Segmentation 117
5.3 Structure of the CULEX Algorithm 118
5.3.1 ROI Extraction 118
5.3.2 Segmentation of the Lumen and Carotid Wall 122
5.3.3 Notes About the CULEX Segmentation Strategy 123
5.4 CULEX Characterization and Image Databases 125
5.4.1 Percent Statistics Algorithm 125
5.4.2 Confidence Interval for the Average Segmentation 126
5.4.3 Segmentation Error 126
5.4.4 Image Database and Ultrasound Equipment 127
5.5 ROI Detection and Robustness to Noise 128
5.6 Segmentation of the Lumen–Intima and Media–Adventitia Interfaces
and IMT Measurement 130
5.6.1 Absolute Segmentation Errors and IMT Measurements 130
5.6.2 Percent Statistics 133
5.6.3 Confidence Interval for the Mean Segmentation 134
5.7 Plaque Imaging 134
5.7.1 Stable Plaques 136
5.7.2 Unstable Plaques (Echolucent Type II) 137
5.8 Discussion and Conclusions 138
References 139
PART III
2-D/3-D Breast Ultrasound Imaging 141
CHAPTER 6
Breast Lesion Classification Using 3-D Ultrasound 143
6.1 Introduction 143
6.2 Level Set Segmentation Method 145
6.2.1 Basic Level Set Theory 145
6.2.2 Summary of Level Set Theory 146
viii Contents
6.3 Proposed Architecture for the CAD System 147
6.3.1 Data Acquisition 148
6.3.2 Preprocessing Subsystem Architecture 148
6.3.3 Segmentation Subsystem Based on the Level Set Framework 149
6.3.4 Beam Angle Statistics 151
6.3.5 Similarity Between Shapes: A Classification Strategy 153
6.4 Experimental Protocol, Data Analysis, and Performance Evaluation 156
6.4.1 Similarity Measure Analysis 156
6.4.2 Performance Evaluation 158
6.5 Discussion 159
6.5.1 Discussion About the Dual Level Set 159
6.5.2 Discussion About the Similarity Measure 160
6.5.3 Discussion About Threshold Selection 160
6.6 Conclusion and Future Works 161
References 161
CHAPTER 7
3-D Breast Ultrasound Strain Imaging 165
7.1 Introduction 165
7.2 Review of the 2-D Continuous Strain Imaging System 167
7.3 Proposed Architecture of the 3-D Strain Imaging System 168
7.3.1 Data Acquisition 169
7.3.2 Image Enhancement Via Sigmoid Mapping 170
7.3.3 Segmentation Subsystem Based on the Level Set Framework 170
7.3.4 Strain Features 174
7.4 Experimental Protocol, Data Analysis, and Results 179
7.4.1 2-D and 3-D Experimental Protocols 179
7.4.2 Segmentation Results 179
7.4.3 Classification Strategy Using ROC Curves and SVM 180
7.4.4 Figure of Merit for 2-D Versus 3-D: Performance Evaluation 181
7.5 Discussions 183
7.5.1 Discussion About the Level Set Framework 183
7.5.2 Discussion About Strain Features 183
7.5.3 Discussion About 3-D Versus 2-D Strain Methods 184
7.6 Conclusion 184
References 185
CHAPTER 8
Automatic Classification of Breast Lesions in 3-D Ultrasound Images 189
8.1 Introduction 189
8.2 Related Works 191
8.2.1 Breast Cancer Diagnosis with Autocorrelation Matrix 192
8.2.2 Solid Breast Nodules with Neural Networks 195
8.3 Theoretical Background 198
8.3.1 Tsallis Entropy 198
8.3.2 The Nonextensive Segmentation Approaches 200
8.3.3 Morphological Chains: Mathematical Morphology Definitions 211
Contents ix