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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5837_Библиотеки_им_академика_М_И_Перельмана.pdf
JuZhang
YunCheng
Despeckling
Methods
forMedical
Ultrasound
Images
Despeckling Methods for Medical Ultrasound
Images
Ju Zhang
•
Yun Cheng
Despeckling Methods
for Medical Ultrasound
Images
123
Ju Zhang
Zhijiang College of Zhejiang
University of Technology
Shaoxing, Zhejiang, China
Yun Cheng
Department of Ultrasound
Zhejiang Hospital
Hangzhou, Zhejiang, China
ISBN 978-981-15-0515-7 ISBN 978-981-15-0516-4 (eBook)
https://doi.org/10.1007/978-981-15-0516-4
© Springer Nature Singapore Pte Ltd. 2020
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part
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This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd.
The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721,
Singapore
Preface
Motivations
Ultrasonic imaging, CT, MRI, and other imaging techniques have been widely used
in clinical diagnosis. Ultrasound imaging technology is safer than other imaging
techniques because it is noninvasive, non-radioactive, convenient, and efficient.
Therefore, the clinical application of ultrasonic imaging technology has become
more important, especially in observing the growth status of the fetus in pregnant
women and diagnosis of lesions of the abdominal organs.
However, the existence of speckle noise has degraded the quality of ultrasound
images and restricted the development of automatic diagnostic techniques. Speckle
noise is an undesirable part of the ultrasound image, which masks the small dif-
ference in gray level and degrades the image quality. Therefore, despeckling is an
important step before the analysis and processing of ultrasound images, and many
researchers are attracted to devote their efforts to this issue.
Main Contributions of the Book
Based on our research work in the past decade in the field of denoising process for
the medical ultrasonic imaging, we present systematically in this book the
despeckling methods for medical ultrasound images. Firstly, the despeckling
methods for medical ultrasound imagings are reviewed and are classified as five
categories, i.e., local adaptive filter, anisotropic diffusion filter, multi-scale filter,
nonlocal means filter, and hybrid filter (nonlocal means and multi-scale hybrid
filter). Secondly, after some basic mathematical tools such as wavelets and shearlets
transformation are introduced, we presented four recently developed despeckling
methods for medical ultrasound images. These presented four methods are wavelet
and fast bilateral filter-based despeckling method for medical ultrasound images,
speckle filtering of medical ultrasonic images using wavelet and guided filter,
v
despeckling method for medical images based on wavelet and trilateral filter, and
nonsubsampled shearlet and guided filter-based despeckling method for medical
ultrasound images, respectively. For these four presented methods, simulations for
simulated pictures and experiments for clinical ultrasonic images are made, and
comparison studies with other well-known existing methods are conducted,
showing the effectiveness and superiority of the presented methods. Students (un-
dergraduate, graduate, and Ph.D. students) and researchers in the field of signal and
image process and also the related doctors in the field of ultrasonic diagnosis in the
hospital will benefit from this book. This book will enable reader to know the state
of the art of the despeckling methods for medical ultrasound images and will
present an useful reference book for their study and research work.
Intended Audience
The book is intended to support graduate courses and the study of Ph.D. and
advanced M.Sc. students in the field of signal and image process. Docto rs in the
field of ultrasonic diagnosis in the hospital will also benefit from this book. Readers
should be familiar with the basics of signal and medical image process. The book
could be also useful for academic researchers wor king in the field of medical image
process, as well as researchers from industrial companies, whose responsibilities
include the development of high-quality automatic diagnostic techniques and
equipment for ultrasound images.
Book Organization
The book is structured as follows:
In Chap. 1, we present an overview of digital image processing, image denoising
technology, the general motivations of despeckling of medical ultrasonic images,
introduction of recent research work on denosing of medical ultrasonic images,
basic principle of ultrasonic image, and several kinds of diasonograph.
In Chap. 2, we will introduce several kinds of denosing methods. Five categories of
despeckle filters are presented. This chapter focuses on the comparison of despeckle
filters for the breast ultrasound images. Despeckle filters which are classified into
five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter,
nonlocal means filter, and hybrid filter) are described. The comparative experiments
of eleven despeckle filters for the two types of simulated images and clinical
ultrasound breast images are presented.
In Chap. 3, a despeckling method which is based on the wavelet transformation and
fast bilateral filter is introduced. An improved wavelet threshold function based on
vi Preface
the universal wavelet threshold function is considered. The Bayesian maximum a
posteriori estimation is applied to obtain a new wavelet shrinkage algorithm.
High-pass component speckle noise in the wavelet domain of ultrasound images is
suppressed by the new shrinkage algorithm. Additionally, the coefficients of the
low-frequency signal in the wavelet domain are filtered by the fast bilateral filter.
Experiments show that the proposed method has improved despeckling perfor-
mance for medical ultrasound images.
In Chap. 4, a new denoising method based on an improved wavelet filter and guided
filter is presented. The coefficients of the low-frequency subband in the wavelet
domain are filtered by guided filter. The filtered image is then obtained by using the
inverse wave let transformation. Experiments with the comparison of the other
seven despeckling filters are conducted. The results show that the proposed method
not only has a strong despeckling ability, but also keeps the image details, such as
the edge of a lesion.
In Chap. 5, an integrated despeckling approach for medical ultrasound images
based on wavelet and trilateral filter is presented. The low-frequency component
of the speckle noise is suppressed by a trilateral filter. It simultaneously reduces the
speckle and impulse noise in real set data. A lot of experiments are conducted on
both synthetic images and real clinical ultrasound images for authenticity.
Compared with other existing methods, experimental results show that the proposed
algorithm demonstrates an excellent denoising performance, offers great flexibility,
and substantially sharpens the desirable edge.
In Chap. 6, a novel despeckling method based on nonsubsampled shearlet
transformation and a guided filter is presented. A nonsubsampled Laplacian
pyramid filter is used to decompose the noisy image thus decomposing the
image into high-freque ncy and low-frequency subbands. Under the direction of the
non-sampling filter bank, a high-frequency subband multi-directional decomposi-
tion is obtained. Based on the threshold function and the correlation of the shearlet
coefficients in the transformation domain, an improved threshold shrinkage algo-
rithm is proposed to perfor m the threshold shrinkage processing on the shearlet
coefficients of the high-frequency subbands. The low-frequency subbands in the
transformation domain are processed by the guided filter, and a denoised ultrasonic
image is obtained by the inverse transformation of the shearlet.
Hangzhou, China Ju Zhang
Yun Cheng
Preface vii
Acknowledgments
The ideas and perspectives that form the heart of this book have continued to evolve
as a result of our research work in the past decade with many colleges and graduate
students. We are deeply grateful to Cheng Wan, Guangkuo Lin, Lili Wu, Yiping
Cheng, Jingliang Chai, Jun Zhou, Zheng Tian, Jingcheng Lv, Hailing Zhou, Jian
Chen, Lunduan Yu, and Chongjian Wu for their efforts in research work in the field
of despeckling of medical ultrasound images. We would like to express our sincere
thanks to Mr. Jingcheng Lv who devoted a significant number of hours to a variety
of aspects of the preparation of this book.
We would thank many people who provided the MATLAB code or executable
file for comparison study in this book. We also express our appreciation to Zhejiang
University of technology and Zhejiang Hospital for providing us support and help
to conduct research work.
The encouragement, patience, technical support provided by Springer Nature
have been crucial in making this book a reality.
ix
Contents
1 Introduction ........................................... 1
1.1 Digital Image Processing
............................... 1
1.1.1 Digital Image Processing and Applications
............ 1
1.1.2 Image Denoising Technology
...................... 5
1.1.3 Signal Convolution
............................. 6
1.2 Motivations
........................................ 8
1.2.1 The Tasks
.................................... 9
1.3 Introduction of Ultrasound Image
........................ 11
1.3.1 Basic Principle of Ultrasound Image ................. 12
1.3.2 Several Kind of Diasonograph
..................... 13
1.4 Book Structure ...................................... 16
References
............................................. 17
2 Despeckle Filters for Medical Ultrasound Images
............... 19
2.1 Models of Speckle Noise for Medical Ultrasound Images
....... 19
2.1.1 Models of Envelope-Detected Echo Signal
............ 19
2.1.2 Models of Speckle Noise
......................... 20
2.2 Despeckle Filters for Ultrasound Images
................... 22
2.2.1 Category 1: Local Adaptive Filter
................... 22
2.2.2 Category 2: Anisotropic Diffusion Filter
.............. 25
2.2.3 Category 3: Multi-scale Filter
...................... 27
2.2.4 Category 4: Nonlocal Means Filter
.................. 29
2.2.5 Category 5: Hybrid Filter (Nonlocal Means
and Multi-scale Hybrid Filter)
..................... 31
2.3 Experiments for Simulation and Ultrasound Images
........... 33
2.3.1 Field II Simulation
.............................. 34
2.3.2 Configuration of Experimental Parameters
............. 34
2.3.3 Full-Referenc e Image Quality Metrics
................ 35
2.3.4 Performances of Different Filters
................... 37
2.3.5 Experiments for Clinical Ultrasound Breast Images
...... 39
xi