Table Of ContentJu Zhang
Yun Cheng
Despeckling
Methods
for Medical
Ultrasound
Images
Despeckling Methods for Medical Ultrasound
Images
Ju Zhang Yun Cheng
(cid:129)
Despeckling Methods
for Medical Ultrasound
Images
123
Ju Zhang YunCheng
Zhijiang Collegeof Zhejiang Department ofUltrasound
University of Technology ZhejiangHospital
Shaoxing, Zhejiang, China Hangzhou, Zhejiang, China
ISBN978-981-15-0515-7 ISBN978-981-15-0516-4 (eBook)
https://doi.org/10.1007/978-981-15-0516-4
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Preface
Motivations
Ultrasonicimaging,CT,MRI,andotherimagingtechniqueshavebeenwidelyused
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
imagesandrestrictedthedevelopmentofautomaticdiagnostictechniques.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
Basedonourresearchworkinthepastdecadeinthefieldofdenoisingprocessfor
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,aftersomebasicmathematicaltoolssuchaswaveletsandshearlets
transformation are introduced, we presented four recently developed despeckling
methodsformedicalultrasoundimages. Thesepresentedfourmethodsarewavelet
and fast bilateral filter-based despeckling method for medical ultrasound images,
speckle filtering of medical ultrasonic images using wavelet and guided filter,
v
vi Preface
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,andPh.D.students)andresearchersinthefieldofsignaland
imageprocessandalsotherelateddoctorsinthefieldofultrasonicdiagnosisinthe
hospitalwillbenefitfromthisbook.Thisbookwillenablereadertoknowthestate
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. Doctors in the
fieldofultrasonicdiagnosisinthehospitalwillalsobenefitfromthisbook.Readers
should be familiar with the basics of signal and medical image process. The book
couldbealsousefulforacademicresearchersworkinginthefieldofmedicalimage
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.
InChap.2,wewillintroduceseveralkindsofdenosingmethods.Fivecategoriesof
despecklefiltersarepresented.Thischapterfocusesonthecomparisonofdespeckle
filters for the breast ultrasound images. Despeckle filters which are classified into
five categories (local adaptive filter, anisotropic diffusion filter, multi-scale filter,
nonlocalmeansfilter,andhybridfilter)aredescribed.Thecomparativeexperiments
of eleven despeckle filters for the two types of simulated images and clinical
ultrasound breast images are presented.
InChap.3,adespecklingmethodwhichisbasedonthewavelettransformationand
fastbilateral filter isintroduced. An improved waveletthreshold function based on
Preface vii
the universal wavelet threshold function is considered. The Bayesian maximum a
posteriori estimation is applied to obtain a new wavelet shrinkage algorithm.
High-passcomponentspecklenoiseinthewaveletdomainofultrasoundimagesis
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.
InChap.4,anewdenoisingmethodbasedonanimprovedwaveletfilterandguided
filter is presented. The coefficients of the low-frequency subband in the wavelet
domainarefilteredbyguidedfilter.Thefilteredimageisthenobtainedbyusingthe
inverse wavelet transformation. Experiments with the comparison of the other
sevendespecklingfiltersareconducted.Theresultsshowthattheproposedmethod
not only has a strong despeckling ability, butalso 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
ofthespecklenoiseissuppressedbyatrilateralfilter.Itsimultaneouslyreducesthe
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.
Comparedwithotherexistingmethods,experimentalresultsshowthattheproposed
algorithmdemonstratesanexcellentdenoisingperformance,offersgreatflexibility,
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
imageintohigh-frequencyandlow-frequencysubbands.Underthedirectionofthe
non-sampling filter bank, a high-frequency subband multi-directional decomposi-
tionisobtained.Basedonthethresholdfunctionandthecorrelationoftheshearlet
coefficients in the transformation domain, an improved threshold shrinkage algo-
rithm is proposed to perform the threshold shrinkage processing on the shearlet
coefficients of the high-frequency subbands. The low-frequency subbands in the
transformationdomainareprocessedbytheguidedfilter,andadenoisedultrasonic
image is obtained by the inverse transformation of the shearlet.
Hangzhou, China Ju Zhang
Yun Cheng
Acknowledgments
Theideasandperspectivesthatformtheheartofthisbookhavecontinuedtoevolve
asaresultofourresearchworkinthepastdecadewithmanycollegesandgraduate
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,LunduanYu,andChongjianWufortheireffortsinresearchworkinthefield
ofdespecklingofmedicalultrasoundimages.Wewouldliketoexpressoursincere
thankstoMr.JingchengLvwhodevotedasignificantnumberofhourstoavariety
of aspects of the preparation of this book.
We would thank many people who provided the MATLAB code or executable
fileforcomparisonstudyinthisbook.WealsoexpressourappreciationtoZhejiang
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-Reference Image Quality Metrics. . . . . . . . . . . . . . . . 35
2.3.4 Performances of Different Filters . . . . . . . . . . . . . . . . . . . 37
2.3.5 Experiments for Clinical Ultrasound Breast Images . . . . . . 39
xi
