Despeckling methods for medical ultrasound images PDF
Preview Despeckling methods for medical ultrasound images
Ju 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 ©SpringerNatureSingaporePteLtd.2020 Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpart of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission orinformationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar methodologynowknownorhereafterdeveloped. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexemptfrom therelevantprotectivelawsandregulationsandthereforefreeforgeneraluse. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained hereinorforanyerrorsoromissionsthatmayhavebeenmade.Thepublisherremainsneutralwithregard tojurisdictionalclaimsinpublishedmapsandinstitutionalaffiliations. ThisSpringerimprintispublishedbytheregisteredcompanySpringerNatureSingaporePteLtd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore 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
The list of books you might like
