剪切力环境下的血管平滑肌细胞图像分割技术研究-英文-解读.doc

2007年4月第44卷第2期四川大学学报(自然科学版)JournalofSichuanUniversity(NaturalScienceEdition)Apr.2007Vol.44No.2文章编号:049026756(2007)0220284205剪切力环境下的血管平滑肌细胞图像分割技术研究李晓宁1,樊瑜波2,3(1.四川师范大学计算机科学学院,成都610068;2.四川大学建筑与环境学院生物力学所,成都610064;3.北京航空航天大学生物工程系,北京100083)摘要:定量分析血管平滑肌细胞VSMCs(VascularSmoothMuscleCells)在剪切力作用下的形态变化有助于理解VSMCs的生长机制以及病理学研究.图像分割在图像分析中扮演着重要角色,但同时也是图像处理流程中最困难的步骤.针对培养在平板流室中的VSMCs相互交叠的细胞图像,作者首先分析了这类细胞图像的特征,随后提出了一种具有四个步骤的图像分割方法,分别是图像预处理、照明校正、数学形态学重建和分水岭转换.实验结果证明,按照这种方法分割细胞图像,取得了较好的效果.关键词:定量分析;分水岭转换;数学形态学;图像分割中图分类号:TP751文献标识码:ACellsegmentationofvascularsmoothmusclecellsundershearstressusingimageprocessingLIXiao2ning,FANYu2bo12,3(1.CollegeofComputerScience,SichuanNormalUniversity,Chengdu610068,China;2.BiomechanicalEngineeringLaboratory,SichuanUniversity,Chengdu610064,China;3.BioengineeringDepartment,BeihangUniversity,Beijing100083,China)Abstract:Quantitativelyanalysisofthemorphologicalresponsetoshearstressonvascularsmoothmusclecells(VSMCs)willfacilitateunderstandingthegrowthmechanismandpathologyofVSMCs.Imagesegmentationplaysakeyroleinimageanalysisandisalsothemostdifficultstepinimageprocessingpipeline.Inthispaper,firstly,thecharacteristicsoftheimagesofVSMCsculturedintheparallelplatechamberisanalyzedindetail,thenonesegmentationstrategywhichhasfourstepsbasedonwatershedoperatorsisproposed.Imageprepro2cessing,illuminationcorrecting,mathematicalmorphologicalreconstruction,watershedtransformationareimplementedaccordingtothesetsequence.Theresultshowsabetterseparationcellsfrombackgroundcanbeachievedbyusingthismethod.Attheendtheresultofimagesegmentationisanalyzedanddiscussed.Keywords:quantitativelyanalysis,watershed,mathematicalmorphology,imagesegmentation收稿日期:基金项目:作者简介:通讯作者:2006210229国家自然科学基金(10527001,10672105);北京R&D项目(H060920051030);四川师范大学基金李晓宁(1972-),男,博士,主要研究方向为医学图像处理.樊瑜波.E2mail:第2期李晓宁等:剪切力环境下的血管平滑肌细胞图像分割技术研究2851IntroductionCellularmechanicsisafocusofnowadaystissueengineeringstudy.Investigationsintotheeffectsofflowonvascularsmoothmusclecells(VSMCs)aremotivatedbythepossibleinfluenceofflowinvascu2larbiologyandpathobiology.Todeterminetheex2tentoffloweffects,studiesinwhichthefluidme2chanicconditionscanbesystematicallyvariedareneeded.Invivostudyisdifficulttoquantitativelydefinethedetailedcharacteristicsofthehemody2namicenvironment.Exertingmechanicalforcethroughvariouskindsofmechanicsdevicesonlivingcellsinvitroisthemainmethodofcellmechanicsnow.Mechanicalstimuli,suchasstrainandfluidshearstress,arebelievedtobekeyfactorsinregu2latingcardiovasculartissuegrowthandremodelingduringdevelopmentandindiseasestates.Shearstressesonvascularsmoothmusclecellsduetobloodfluidflowplayavitalroleinregulatingtheirmor2phology,structure,growthrateandfunctions.Thechangeinmorphologyisaninstinctiveresponsetobiomechanicalenvironmentandisanindicatorforthefunctionalchangesinthecells.Quantitativelyanalyzingcellmotilityandmorphodynamicproper2tiesfacilitatesunderstandingtherelationsbetweenmechanicalstimuliandmechanismsofcellpathologi2calchanges.Itisnecessarytobuildadigitalimageprocessingsystemwiththeaidofelectronictech2nologiesandimageprocessingforthistarget.Howtoexactlyandaccuratelysegmentcellsfromimagesisthemajorproblemwiththeimageprocessingsys2tem.Imagesegmentationisoneofthehotspotsanddifficultstepofimageprocessingtechnology.Uptonowthousandsofmethodsofimagesegmentationhavebeenpresented.Butunfortunatelythereisnomethodappreciatedforeachkindofimagesegmen2tationproblems.Althoughsomealgorithmsarepro2posedfordyeingandfixingcellimagesegmentationtheyareunsuitablefortheimagesofbiologicalactivecells.Intheexperimentsofcellularmechanicsthematerialsoftenmustbekeptaliveandcouldnotbeendyed.Livingcellsaretransparentandtheirimagepropertiesvarywithexperimentalconditions.Theseallinducethedifficultiesfortheimagesegmenta2tion.Sodevelopingthesegmentationstudiesfortheimagesoflivingcellsculturedinthemechanicalen2vironmentisveryimportant.Thisarticleexploresthetechnologiesofimagesegmentationofvascularsmoothmusclecells(VSMCs).2Materialsandmethod2.1CellcultureVascularsmoothmusclecellswereobtainedfromrataortaaccordingtoageneralmethodusedinmedicine.Aninnovativeparallelplateflowchambersystemisusedtoculturethesecollectedcellsandtodeliverquasi2physiologicalpulsatileflowfieldforcul2turedvascularcells1.2.2ImageacquiringsystemImageacquiringsystemconsistsofaphasecon2trastinvertedmicroscope(OlympusIX70,Japan),aprofessionalmicroscopyCCD(PixeraPro150ES,USA)andapersonalcomputer.AnalogcellimagesobservedbyOlympusIX70microscopeareconvertedtodigitalimagesthroughthevideooutputcompo2nent,andthesingleporttube(IX2SPT,Japan),aCmountandPixera150ESdigitalCCD.Atlastimagesarecapturedintocomputersystems.DiagramofourimageacquiringsystemisshownasFig.1.Fig.1DiagramoftheimageacquiringsystembasedonprofessionalmicroscopyCCD2.3CharacteristicsofvascularsmoothmusclecellimagesBeforesegmentationstrategyisdetermined,studyingthecharacteristicsofcellimagesisneces2sary.Generally,cellsimagespresentintwoclassicalmodes.Oneisoverlappedandtheotherisnon2over2lapped.Fornon2overlappedcellsimage,onemethod286四川大学学报(自然科学版)第44卷isintroducedinouranotherpaper2.Asfortypicaloverlappedcellsimages,thereareseveralnotableandcommoncharacteristics.(1)Fromthewholeimage,thecontrastofintensitiesbetweenthecellsareaandthebackgroundislow.Althoughtherearesomehalationbetweencellsandbackground,thein2tensitiesofhalationisnotconstantandtheshapeofhalationisnotregular.(2)Theboundariesofthecellsmaybeextremelydifficulttodefineduetothevariationsofcellmorphology.(3)Manyholeslieinthebodyofcell.Andsotheintensitiesofsomeareasofcellbodyareidenticaltothoseofbackground.(4)Cellsaregrowinginoverlappingmode.Andthisconducesenormouslyimpedimentforseparatingcellsfromtheimages.2.4SegmentationStrategyObviously,itisimpossibletoaccuratelydistin2guishthecellboundaryfromthebackgroundonlybysimplemethods.Wementionthatwatershedisuse2fultoseparateoverlappingobjectsfromthescene.Sowedevelopthispipeline(Fig.2)tocompletethesegmentationtaskasfollows.Fig.2Illustrationoftheappliedimageprocessingpipeline2.4.1ImagepreprocessingInputimageisfirstlyenhancedbyaself2multiplemethod.TheRGBval2uesofeachpixelaremultipliedbythemselvesindi2viduallyandtheproductsarerespectivelynormalizedwithintherangefrom0to255.Thenormalizedval2ueisthenewintensityofthepixel.Thisprocedureisdonepixelbypixelandthengreytransformationisusedforeachpixeloftheconsequentimage.ThegrayscaleimageisshowninFig.4(a).2.4.2CorrectinginhomogeneousilluminationAlargenumberofexperimentfactorscancauselight2ingtobeeven,soinhomogeneousilluminationisacommonprobleminquantitativemicroscopy.Addi2tiveilluminationinfluencescanbenormalizedbyre2movinglocalmeansorutilizingderivativesforobjectdetecting3.However,inthecaseofdiascopicmi2crographsthereisamultiplicationrelationshipbe2tweenthemainlyhigh2frequencycellobjectinforma2tionandratherlow2frequencyvariationsofillumina2tion.Logarithmicimagemodelissuitableforremov2ingmultiplicativeilluminationcomponents.Thisre2lationcanbeexpressedasF(x,y)=(g-A(x,y)3I(x,y)WheretheimagefunctionF(x,y)isregardedastheintensityoflightpassingthroughalightab2sorbingsample.A(x,y)andI(x,y)denotetheabsorptionandmicroscopeillumination,respective2ly.Theconstantgrepresentsthemaximumdigitalintensityvalue,whichis255for82bitimages.Inthelogarithmicdomain,itcanbedescribedasA(x,y)=g-exp(logF(x,y)-log(I(x,y)AccordingtotheworkofVolkerMetzleretc.4,morphologicalilluminationcorrectionhasmanyadvantagesoverlinearlow2passfiltering.TheirmethodillustratedinFig.3isusedtocorrecttheinhomogeneousilluminationwedealwithandgoodresultisobtained.Onedifferencefromtheirmethodsisthesizeofstructureelementthatweadoptinmorphologicalreconstructionstep.Wehavementionedthatinourcapturingconditionsthesizeofcellbodyisalwayssmallerthan50pixels,soweFig.3Flowchartoftheilluminationbymorphologicalmethod第2期李晓宁等:剪切力环境下的血管平滑肌细胞图像分割技术研究287Fig.4Resultimages(a)Graytransformation,(b)Illuminationcorrectionbymorpho2logicalmethods,(c)Morphologicalreconstruction,(d)Distancetransformationfor(c),(e)Markerimagebydomeextraction(h=4),(f)Overlayingmarkerimageondistanceimageerodetheimageusingstructureelementwhichsizeis25pixelsandconsequentlythecellularpixelinfor2mationiserased.Thenwereconstructtheimagebygeodesicdilationandcangetanimageconsistingofmostbackgroundinformation.Ifwereducetheo2riginalimagewiththisconsequentlyone,wecanob2tainanimagethatonlyincludescellbodies.Theob2tainedimageisshowninFig.4(b).AsshowninFig.4(b),althoughcellbodiesareseparatefrombackground,therearesomeback2groundareasarealsoretainedwhichisnotwanted.Soweutilizeanothermorphologicalreconstructionoperatortoeraseallthebackgroundareas.Differentfromlaststep,thisreconstructionoperatorisappliedtorestorecellbodyinformationandtogetridofbackgroundcomponentornoise.Becausecellsareconjointtogetherandbackgroundareaornoiseisin2solate,wecanerodetheimagewithlargestructureelementandtheobtainedimageislookedasmakerimagewhichsignallthecellareas.ReconstructiontheimageshowninFig.4(b)usingthismakerim2age,canclearupallthebackgroundandnoise.TheobtainedimageisshowninFig.4(c).Inthisimageonlycellareasarepresented.2.4.3WatershedtransformationAlthoughthebackgroundandnoisearealleliminated,cellsareoverlappedanditisnecessarytoseparatethemfromeachother.Watershedtransformationiseffectiveforsolvingthiskindofproblems,butinfact,thebrutalcomputationofwatershedsdoesnotconstituteagoodsegmentationmethod.Indeedthesimplecom2putationofanimageswatershedsmostlyresultsinanover2segmentation.,i.e.,thecorrectcontourslostinamassofirrelevantones.Inordertogetridofthisover2segmentation5,onemustmaketwopreparations.Oneistransformingoriginalimagetogradientoneordistanceone,whichwillbeinputimageofwatershed.Weusedistanceimagethoughdistancetransformationasinputimageofwater2shed.ThecorrespondingdistanceimageisshowninFig.4(d).Theotherisextractingmakerpointswhichsignobjectstobedissected.Universallyonemakerpointindexesoneobject.Onehastousetheknowledgeavailableontheproblemtodesignaro2bustalgorithmforextractingmarkersofdifferentre2gionstobesegmented.Inthispaper6,h2dometransformationispresentedanditisverifiedthath2dometransformationcanextractlightstructureswithoutinvolvinganysizeorshapecriterion.Theonlyparameter(h)isrelatedtotheheightofthesestructures.Wedesignadometransformationwhichparameter(h)isequalto4andutilizeittogetmarkerpointsfromdistanceimage.Aftersuperpos2ingthemakerimage(Fig.4(e)withFig.4(d)andthenreversetheconsequentimagewecangetimageasshowninFig.4(f).Watershedoperatorisappliedtothisimage(asFig.4(f).TheresultisshownFig.5(a).3ResultsanddiscussionAsshowninFig.5(a),thesegmentationlinesarepresentedinmanyconjunctionofcellbound2aries.Wecanlabelthesegmentedimageandgetthecontoursofcellbodyusingotherimageprocessingtechniques.Hence,quantitativeanalysiscanbesuc2ceeded.288四川大学学报(自然科学版)第44卷Fig.5Resultsofwatershedsegmentationwithdifferentmakerimages(a)h=4;(b)h=3;(c)h=5Asawhole,thelocationsofsegmentationlinesarecoincidentwiththeintuitionofthehumaneye2whendomeheightequalsto4.Butsomewhereseg2mentationlineslocateincongruously.Becausetheaccurateofwatershedseparationmethodseriouslydependsonthemakerimage,wetriedthreeheightsofdomewhichareparameterh=3,4,5,respec2tively.TheresultsareshowninFig.5(b),Fig.5(a),Fig.5(c).Itisevidentthatover2segmentationispresentedath=3andsomecellarenottotallyseparatedatborderath=5.Comparedwiththem,theresultisbetterath=4.Reference:1ZhuangFG.Recentad