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外文资料-- Diagnosis Technology Research Of Mammographic Masses in Content-based Image Retrieval.PDF

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外文资料-- Diagnosis Technology Research Of Mammographic Masses in Content-based Image Retrieval.PDF

DIAGNOSISTECHNOLOGYRESEARCHOFMAMMOGRAPHICMASSESINCONTENTBASEDIMAGERETRIEVALSONGLIXIN,WANGQINGYANCOLLEGEOFELECTRICALELECTRONICENGINEERINGHARBINUNIVERSITYOFSCIENCEANDTECHNOLOGYHARBIN,CHINALIXINS99YAHOOCOMCNWANGLIDEPTOFGYNECOLOGYHEILONGJIANGHOSPITALHARBIN,CHINAWANGLI1439163COMWANGQINGYAN1984163COMABSTRACTINORDERTOASSISTDOCTORTODIAGNOSISOFMAMMOGRAPHICMASSES,AMETHODISPROPOSED22FEATURESAREEXTRACTEDFROMEACHQUERIEDREGIONOFINTERESTROIAKNEARESTNEIGHBORKNNALGORITHMISUSEDTORETRIEVESIMILARIMAGESFROMDATABASE,ANDFURTHERCALCULATETHEMUTUALINFORMATIONMIBETWEENTHEQUERIEDIMAGEANDTHEIMAGESWHICHAREINTHERETRIEVALRESULTS,SOASTOIMPROVETHERETRIEVALPERFORMANCEFINALLY,THESCHEMETAKESTHEFIRSTNINEIMAGESWITHTHEHIGHESTMISCORESASTHEFINALRETRIEVALRESULTSWITHTHEPURPOSEOFPROVIDINGAVAILABLEDECISIONMAKINGINFORMATIONOFDIAGNOSTICAIDS,WECOMPAREANDANALYZETHREECALCULATINGMETHODSOFDECISIONINDEXTHEEXPERIMENTRESULTSSHOWTHATTHEMETHODISBETTERTHANMETHODOFUSINGKNNONLY,ANDIMPROVETHEACCURACYOFDIAGNOSISEFFECTIVELYKEYWORDSMAMMOGRAPHYIMAGE;CONTENTBASEDIMAGERETRIEVAL;COMPUTERAIDEDDIAGNOSIS;MUTUALINFORMATIONIINTRODUCTIONSCREENINGMAMMOGRAPHYISCONSIDEREDTHEMOSTRELIABLEANDEFFECTIVEMETHODINTHEEARLYDETECTIONOFBREASTCANCERHOWEVER,MAMMOGRAPHYIMAGEISBLURRYANDITSCONTRASTISLOWDUETOIMAGINGPRINCIPLE,MANYSMALLERLESIONSARENOTEASYTOBEOBSERVEDANDEXTRACTEDOWINGTOTHEONESWHICHHAVEBEENOVERWHELMEDBYTHENORMALBREASTTISSUETHEREFORE,EARLYDETECTIONANDDIAGNOSISOFBREASTCANCERRELIEDHEAVILYONTHERADIOLOGIST’SSUBJECTIVEVIEWSCOMPUTERAIDEDDETECTIONTECHNOLOGYPROVIDESANVALUABLE“SECONDREVIEW”1FORRADIOLOGISTSINTHEEARLYDETECTIONPROCESSOFBREASTCANCERTHETRADITIONALCADSYSTEMOFBREASTMASSESDETECTIONGENERALLYBASEDONARTIFICIALNEURALNETWORKTHEMETHODOFCONTENTBASEDIMAGERETRIEVALNOTONLYELIMINATEDTHEBURDENOFDESIGNINGANDTRAININGNEURALNETWORKCLASSIFIERS,BUTALSOMADEFULLUSEOFPASTDIAGNOSISOFBREASTMASSESOFTHEORIGINALEMPIRICALDATAACCORDINGLYITCANEFFECTIVELYHELPPHYSICIANSIMPROVETHEACCURACYOFTUMORDIAGNOSISALTHOUGHCBIRHASBEENAPPLIEDINANUMBEROFFIELDS,THEBREASTIMAGESWHICHARECHARACTERIZEDBYHIGHERRESOLUTION,BIGGERIMAGESIMILARITYANDMOREINFORMATIONAREDIFFERENTFROMOTHERSITISDIFFICULTTORETRIEVESIMILARIMAGEWITHTHESAMEPATHOLOGICALCHARACTERISTICSFROMIMAGELIBRARYITISTHEREFOREESSENTIALTOESTABLISHARETRIEVALSYSTEMWITHMEDICALPRACTICEMAMMOGRAPHYOFCBIRISSTILLASTUDYINGDIRECTIONSOMERESEARCHINSTITUTESHAVEBEENCARRIEDOUTRELEVANTRESEARCHBINZHENG2HASPROPOSEDANINTERACTIVERETRIEVALMETHODWHICHIMPROVEDTHEVISUALSIMILARITYBETWEENTHERESULTIMAGESANDTHEQUERIEDIMAGESGEORGIADTOURASSI3ADOPTEDMUTUALINFORMATIONASTHESIMILARITYMEASUREMENTAMONGIMAGES,CLASSIFIEDTHEREGIONOFINTERESTBYTHEMOSTSIMILARIMAGESOFRETRIEVALHILARYALTO4STUDIEDTHEVARIOUSCOMBINATIONSOFSHAPE,TEXTUREANDEDGESHARPNESSFEATURES,WHICHUSEDINCLASSIFYINGBENIGNANDMALIGNANTTUMORMASSESTOIMPROVETHERETRIEVALACCURACYANDACHIEVETHEDIAGNOSIS,THISPAPERSTUDIESACOMBINATIONOFKNNMIRETRIEVALMETHODOFBREASTMASSESDETECTION,ANDGIVESADECISIONINDEXOFAIDEDDIAGNOSISONTHEBASISOFTHISSTUDYIICONTENTBASEDMAMMOGRAPHICMASSESIMAGERETRIEVALAFTERSUBMITTINGAQUERIEDROIIMAGE,THESYSTEMWILLAUTOMATICALLYEXTRACT22FEATHERSWHICHMATCHTHEFEATHERSINTHEDATABASE,OBTAINSEVERALFRONTIMAGESACCORDINGTOTHESIMILARITYFROMHIGHTOLOW,ANDFINALLYCALCULATETHEDECISIONINDEXANDANALYZETHEPATHOLOGICALINFORMATIONOFMASSESACCORDINGTOTHERETRIEVALIMAGESANDTHEIRPATHOLOGICALINFORMATIONTHEOVERALLPROCESSOFTHEMETHODISSHOWNINFIG1FIGURE1THEDIAGRAMOFTHEOVERALLPROCESSFIG2SHOWSTHERESULTOFTHERETRIEVALTHEQUERIEDROIISINTHEUPPERLEFTANDTHEPATHOLOGICALINFORMATIONISTHEFOLLOWINGTHEFIRSTNINEROISAREINTHELEFTANDTHELETTERSABOVEEACHROIMEANCLASSES,“M”MALIGNANT,“B”BENIGNAFEATUREEXTRACTIONTHECONVENTIONALCONTENTBASEDRETRIEVALAIMEDATIMPROVINGVISUALSIMILARITYBETWEENTHERETRIEVALIMAGESANDTHEQUERIEDIMAGE,BUTTHEBREASTXRAYIMAGESAREVISUALLYVERYSIMILAR,SOFEATURESELECTIONPROCESSSHOULDNOTBEBASEDSOLELYONTHEVISUALSENSEOFTHESIMILARITIESDOCTORSALSOTEND9781424447138/10/25002010IEEETOLIKETHESAMEKINDOFIMAGESASTHESIMILARIMAGES,WHICHISTHESIMILARITYINTHEMEDICALSENSETHEREFORE,THISFEATURESELECTIONBASEDONTHEFOLLOWINGPRINCIPLESIFAFEATUREISVALIDINCLASSIFICATION,ITISALSOVALIDINRETRIEVALTHUS,THEMOREWHICHARETHESAMEWITHTHEQUERIEDIMAGESINTHERESULTS,THEMOREEFFECTIVETHERETRIEVALISFIGURE2THERESULTOFTHERETRIEVALAFTERTHEQUERYROITOBEOBTAINED,IFTOCALCULATETHERELEVANTCHARACTERISTICSOFASUSPICIOUSLUMP,THENITREQUIRESSEGMENTATIONOFSUSPICIOUSMASSESTHESEGMENTATIONOFSUSPICIOUSMASSESISDIVIDEDINTOTHREESTEPSINTHISTEST1REMOVETHE“BACKGROUNDTREND”5OFTHEQUERIEDROIIMAGE;2RESTRAINTHEADJACENTTISSUESOFSUSPICIOUSMASSES;3SEGMENTTHESUSPICIOUSMASSWITHTHEIMPROVEDMULTILAYERTOPOGRAPHICSEGMENTATION6AFTERTHAT,22FEATURESAREEXTRACTEDFROMEACHROIASTHEFEATHERSET,INCLUDINGBINZHENG’SETAL2TWELVEFEATHERS,NICHOLASPETRICK’S7SEVENFEATHERSANDRENCHAOJIN’S8THREEFEATHERSBSIMILARITYMEASUREMENTTHEIMAGESIMILARITYMEASUREISTHESIMILARITYBETWEENTHEIMAGEFEATURESTHESIMILARITYMEASUREMENTMETHODSWILLHAVEADIRECTIMPACTONTHEPERFORMANCEOFIMAGERETRIEVALTHISPAPERPROPOSESACOMBINATIONOFKNNWITHMISIMILARITYMATCHINGALGORITHM1KNEARESTNEIGHBORALGORITHMAMULTIFEATUREKNEARESTNEIGHBORKNNBASEDALGORITHMWASAPPLIEDTOSEARCHFORTHE“COMPUTATIONALLYSIMILAR”ROISINTHEREFERENCELIBRARYSIMILARITYWASMEASUREDBYTHEDIFFERENCEINFEATUREVALUES,RIFXBETWEENAQUERIEDQROIYANDAREFERENCEIROISXINAMULTIDIMENSIONALNFEATURESPACE,21,NQIRQRIRDYXFYFX−∑1THESMALLERTHEDIFFERENCE“DISTANCE”,THEHIGHERTHEDEGREEOFTHECOMPUTED“SIMILARITY”ISBETWEENANYTWOCOMPAREDREGIONSTHECOMPUTEDDISTANCESBETWEENATESTQUERIEDREGIONANDEACHOFTHESTOREDREFERENCEREGIONSWERESORTEDRANKORDEREDFROMTHESMALLESTTOTHELARGESTTHEFIRSTKREGIONSINTHELISTWERETHENSELECTEDASTHEK“MOSTSIMILAR”ORTHEBEST“MATCHED”REFERENCEREGIONSADISTANCEWEIGHTWASDEFINEDAS020201,,1,QIIDDDYXWDDD⎧⎪⎪⎨⎪≤⎪⎩ANDTHECLASSIFICATIONSCORE,ORTHEPROBABILITYTHATAREGIONISACTUALLYMALIGNANT,WASCOMPUTEDAS111MIIMNIJIJWPWW∑∑∑,KMN2WHERENISTHENUMBEROFMALIGNANTMASSREGIONSANDMISTHENUMBEROFBENIGNMASSREGIONSTHATWERESELECTEDINTHESETOFK“MOSTSIMILAR”ROIS2MAXIMUMMUTUALINFORMATIONMETHODTHERESULTSRETRIEVEDBYKNNMETHODAREFURTHERMATCHEDBYTHEMAXIMUMMUTUALINFORMATIONMETHOD,ANDTHEFINALRESULTISBETTERTHANOTHERSBYUSINGKNNONLYTHECORRELATIONBETWEENTWORANDOMVARIABLESENTROPY,ISALSOKNOWNASMUTUALINFORMATIONMUTUALINFORMATIONBETWEENTWORANDOMVARIABLESCANSERVEASASTATISTICALMEASUREOFCORRELATIONINTHEPREVIOUSSTUDY,IMAGERETRIEVAL,MUTUALINFORMATIONALSOHASBEENAPPLIEDTOCONTENTBASEDMEDICALIMAGERETRIEVAL,ANDHASACHIEVEDRELATIVELYGOODRESULTSGIVENTWOIMAGESXANDY,THEIRMIIX;YISEXPRESSEDAS2,,,LOGXYXYXYXYPXYIXYPXYPXPY∑∑3WHEREPXYX,YISTHEJOINTPROBABILITYDENSITYFUNCTIONPDFOFTHETWOIMAGESBASEDONTHEIRCORRESPONDINGPIXELVALUESPXXANDPYYARETHEMARGINALPDFSTHEBASICIDEAISTHATWHENTWOIMAGESAREALIKE,THEMOREINFORMATIONXPROVIDESFORYANDVICEVERSATHEREFORE,THEMICANBETHOUGHTASANINTENSITYBASEDMEASUREOFIMAGESSIMILARITYIFTHEQUERYIMAGEXANDASTOREDIMAGEYDEPICTSIMILARSTRUCTURES,THENTHEPIXELVALUEINIMAGEXSHOULDBEAGOODPREDICTOROFTHEPIXELVALUEATTHECORRESPONDINGLOCATIONINIMAGEYCONSEQUENTLY,THEIRMISHOULDBEHIGHASSHOWNINEQ3,THEMIESTIMATIONOFTWOMAMMOGRAMPHICROISREQUIRESCOMPUTATIONOFTHEJOINTANDMARGINALPDFSWEFOLLOWEDTHEHISTOGRAMAPPROACH9FORTHETASKSINCETHEIMAGESOFDIGITALDATABASEFORSCREENINGMAMMOGRAPHYDDSMCONSIDEREDINOURSTUDYARE12BITIMAGES,THEPDFSWEREESTIMATEDUSINGAREDUCEDNUMBEROF256EQUALSIZEDINTENSITYBINSTOAVOIDPOTENTIALOVERESTIMATIONERRORS10THISISATYPICALPRACTICEFORMIESTIMATIONINIMAGEREGISTRATIONCDECISIONINDEXBESIDESTHERETRIEVEDROIIMAGES,THEDECISIONINDEXDIINDICATINGTHERELATIVEPROBABILITYTHATAROICONTAINSAMASSCANBECALCULATEDAUTOMATICALLYWITHAFORMULAANDOUTPUTTOTHEUSERAHIGHERDIVALUEMEANSAHIGHERPROBABILITYTHATTHEROICONTAINSAMASSTHEFORMULAFORCALCULATINGTHEDIISBASEDONTHEMETHODSPROPOSEDBYGEORGIADTOURASSIETAL31211{,1}{,1}{,1}MQIIIQMNQIIQJJIJSYXKRXDIYSYXKRXSYXKRX−−−∑∑∑4WHEREMISTHENUMBEROFIMAGESRETRIEVEDFROMTHEDATABASETHOSECONTAINMASSROISNISTHENUMBEROFIMAGESRETRIEVEDFROMTHEDATABASETHOSECONTAINNORMALROISKMNRANKXIISTHEORDERINGNUMBEROFXIWHENTHERETRIEVEDROIIMAGESARESORTEDINDESCENDINGORDERITCANBESEENTHATFOREITHERMETHOD,THEHIGHERDIMEANSAHIGHERPROBABILITYTHATTHEROICONTAINSAMASSDI2CONSIDEREDTHEFACTOROFORDERINGNUMBEROFXIANDASSIGNEDARIGHTTOEACHOFSIMILARITYMEASUREVALUESANDITGIVESABETTERPERFORMANCEINOUREVALUATIONEXPERIMENTS,SOWETAKEITASOURINITIALDECISIONINDEXIIIEXPERIMENTALRESULTSANDANALYSISROIINTHEIMAGEDATABASECOMESFROMDDSMOFUNIVERSITYOFSOUTHFLORIDATHEROIDATABASEINCLUDES514MALIGNANTROISAND321BENIGNROISEACHROIISILLUSTRATEDINANIMAGEWITHSIZEOF125125PIXELSTHEDEPTHOFIMAGEIS12BITSEACHROICONTAINSATMOSTONEMASSNOMASSINTHEROIWEEXTRACTEDISONTHECHESTWALLRECALLRATEANDPRECISIONRATEISTHESTANDARDINFORMATIONRETRIEVALEVALUATIONMETHODTHENUMBEROFIMAGESRETURNEDKTAKESAVERYIMPORTANTEFFECTFORTHEPERFORMANCEOFKNNRETRIEVALSYSTEMTHEAVERAGEPRECISIONRATEWILLBECALCULATEDTOOBTAINANOPTIMALKVALUEFROMFIG3ITISCONCLUDEDTHATPRECISIONRATEISNOTMUCHDIFFERENTFORTHEDIFFERENTKVALUESHOWEVER,CONSIDERINGTHEMUTUALINFORMATIONMATCHINGFORTHERESULTS,THERELATIVELYSMALLANDTHEPRECISIONVALUESLIGHTLYHIGHKISSELECTED,K25FIGURE3THEAVERAGEPRECISIONOFDIFFERENTKVALUESATHRESHOLDVALUEISUSEDASADIVIDINGPOINTBETWEENBENIGNANDMALIGNANTMASSES,ANDTHEFIG4SHOWSTHEDISTRIBUTIONOFBENIGNANDMALIGNANTMASSESOFDECISIONVALUESINTHEDATABASETHEREISNOCLEARDEMARCATIONPOINTBETWEENTHEMASSESFROMTHEHISTOGRAMTHUS,ATHRESHOLDVALUESHOULDBEDEFINEDBETWEEN0AND1THE“ERRORRATE”ISDEFINEDBYTHENUMBEROFMALIGNANTROIOFDI<TANDTHENUMBEROFBENIGNROIOFDI≥T,ACCORDINGLYBYTHENUMBEROFWRONGDECISIONUNDERTITISCHANGINGWITHT,ANDCANBEFOUNDOUTBYEXHAUSTIVEATTACKMETHODFIGURE4THEHISTOGRAMOFDECISIONINDEXTHENARECEIVEROPERATINGCHARACTERISTICROCCURVECANBEPLOTTEDTOEVALUATETHEPERFORMANCEOFUSINGOURSYSTEMTOCLASSIFYBETWEENTRUEPOSITIVEANDFALSEPOSITIVEMASSREGIONSTHEAREAUNDERROCCURVEAUCVALUEISUSEDASTHEINDEXOFPERFORMANCETHELEAVEONEOUT11SAMPLINGSCHEMEANDTHEROCCURVEANALYSISAREUSEDFORTHEASSESSMENTOFOURSYSTEMEACHTIMEAROIIMAGEISCHOSENFROMTHEDATABASEFIGURE5THEROCCURVEOFTWOMETHODSINDIFFERENTDIESASTHEQUERYROIIMAGE,THENTHERESTROIIMAGESFORMATESTDATABASETHEPROCEDUREISPERFORMEDREPEATEDLY,EACHTIMEAROIIMAGEINTHEDATABASEISCHOSENASAQUERYIMAGEFIG5SHOWEDTHEROCCURVEOFTWOMETHODSINDIFFERENTDIESTABISHOWEDTHEAZVALUESOFTWOMETHODSINDIFFERENTDIESANALYSINGANDCOMPARINGTHISTHREEMETHODSDI1ADDSSOMEEFFECTSOFSIMILARIT

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