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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’S8THREEFEATHERSBSIMILARITYMEASUREMENTTHEIMAGESIMILARITYMEASUREISTHESIMILARITYBETWEENTHEIMAGEFEATURESTHESIMILARITYMEASUREMENTMETHODSWILLHAVEADIRECTIMPACTONTHEPERFORMANCEOFIMAGERETRIEVALTHISPAPERPROPOSESACOMBINATIONOFKNNWITHMISIMILARITYMATCHINGALGORITHM1KNEARESTNEIGHBORALGORITHMAMULTIFEATUREKNEARESTNEIGHBORKNNBASEDALGORITHMWASAPPLIEDT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
