一种基于广义Gamma分布的医学图像检索系统(IJIGSP-V7-N6-7)

I.J.Image,GraphicsandSignalProcessing,2015,6,52-58PublishedOnlineMay2015inMECS()DOI:10.5815/ijigsp.2015.06.07Copyright©2015MECSI.J.Image,GraphicsandSignalProcessing,2015,6,52-58AnEfficientSystemforMedicalImageRetrievalusingGeneralizedGammaDistributionT.V.MadhusudhanaRaoDepartmentofCSE,T.P.I.S.T.,Bobbili,A.P.,INDIAEmail:[e-mail]S&SE,AndhraUniversity,Visakhapatnam,INDIAEmail:[e-mail],GITAMUniversity,Visakhapatnam,INDIAEmail:[e-mail]—Efficientdiagnosisplaysacrucialrolefortreatment.Inmanycasesofcriticalness,radiologists,doctorsprefertotheusageofinternettechnologiesinordertosearchforsimilarcases.AccordinglyinthispaperaneffectivemechanismofContentBasedImageRetrieval(CBIR)ispresented,whichhelpstheradiologists/doctorsinretrievingsimilarimagesfromthemedicaldataset.Thepaperispresentedbyconsideringbrainmedicalimagesfromamedicaldataset.Featurevectorsaretobeextractedefficientlysoastoretrievetheimagesofinterest.Inthispaperatwo-wayapproachisadoptedtoretrievetheimagesofrelevancefromthedataset.InthefirststeptheProbabilityDensityFunctions(PDF)areextractedandinthesecondsteptherelevantimagesareextractedusingcorrelationcoefficient.Theaccuracyofthemodelistestedonadatabaseconsistingof1000MRimagesrelatedtobrain.TheeffectivenessofthemodelistestedusingPrecision,Recall,ErrorrateandRetrievalefficiency.TheperformanceoftheproposedmodeliscomparedtoGaussianMixtureModel(GMM)usingqualitymetricssuchasMaximumdistance,MeanSquaredError,SignaltoNoiseRatioandJaccardquotient.IndexTerms—GeneralizedGammaDistribution,ContentBasedImageRetrieval,Relevancy,Correlation,QualitymetricsI.INTRODUCTIONBraindiseaseisoneofthemoststrikingfactorsfortheincreaseinmortalityrates.InIndiathisrateshavebeenincreasingcontinuously[24].MostofthediseasescausingbraintumorsmaybeeitherBenignorMalignant.Theclassificationsofthesearesubjectedtothesizeofthetumors.Henceeffectivediagnosisandidentificationofthesediseaseswillbeofcrucialimportance.MostofthecasesrelatedthetreatmentofthesediseasesissubjectedtoMRimaging.MRimagingismainlychoosingbecauseofitspropertyofnon-ionization.Alongwiththeadvantage,thegraterdisadvantagewiththesetechniquesisthatthereportsaregeneratedbytheradiologistsusingthevisualperceptionoftheMRimages[22].Howeverasthenumberofcasesofbrainrelateddiseasesareincreasing,thevisualperceptionmayleadtoincorrectdecisions.Anotheraddedfactortothisistheproportionateincreaseoftheradiologistsversustheincreaseinthenumberofcasesislagging.Hencetheadvantageofusingautomatedsystemsforeffectivebrainimagescanningandreportgenerationsareofcrucialimportance.Thispaperhighlightsamethodologywhichhelpstoretrievetherelevantimagesfromthedatabases.Thissystemmayhelpinparticulartotheparamedics,radiologistresidinginremoteareastosuggestabasictreatmentforpatientinresidinginruralareas.Sothathe/shecanbeshiftedtonearbysuperspecializedhospitals.ManysystemshavebeenpresentedintheliteraturebasedonPictureArchiveandCommunicationSystems(PACS)[18].Alsotextbasedretrievalsystemsarelistedintheliteraturewhereintherelevanttext/keywordsfromthelabreportsareretrievedagainstaquery.Howeverthesesystemshavetheirowndrawbackssincetextannotationisdifficult[14],though,theusageoftextwillbeusefultosomeofthepatientseagertoknowaboutsomerelatedfactsabouttheirdisease.Hence,inthispaperwepresentanovelmethodologyofCBIRusingGeneralizedGammaDistributionwhichhelpstoretrievesimilarimagesbasedoncontentaswellastext.TheupdatedequationsofthemodelparametersareestimatedusingExpectationMaximization(EM)algorithm.Themainadvantagebehindtheusageofthisdistributionisofthefactthatthehumantissuesareasymmetricinnatureanditcanhandlethespecklesmoreefficiently.Assumingthattheshapeandtexturefeaturesplayavitalroleinidentifyingthecontentsmoreeffectively[20][21],inthismodelidentificationofMalignantandnon-Malignanttissuesisexperimented.Othermodelsbasedonautoregression[7],wavelets[19],semanticgap[10][13],relevancefeedback[23],Gaborfilter[4],K-AnEfficientSystemforMedicalImageRetrievalusingGeneralizedGammaDistribution53Copyright©2015MECSI.J.Image,GraphicsandSignalProcessing,2015,6,52-58meansalgorithm[3][16],Geneticalgorithm[9],KD-Tree[8],binarysplitting[2],quadtrees[11]havebeenproposedintheliterature.However,thesemodelslackinefficiencywhileretrievingtheimagesofrelevancebecauseofseveraldisadvantageslikeK-meansalgorithmissensitivetoinitialclusterswhichmaynotbeefficientforhandlingmedicalimageswherethedataisbothcontinuousanddiscrete.Modelslikebinarysplitting,KD-Tree,Geneticalgorithmsaresubjectedtoparametricdependenceandcomplexity[5][15].Henceefficientmethodstoretrievetheimagesofinterestwillbeofagreatadvantage.A.TheRelatedWorkModelsbasedonGamma,Log-NormalandNakagamidistributionsarealsohighlightedintheliteratureforhandlingasymmetricdistributions.However,Nakagamidistributionoverrulestheotherdistributionswhilecharacterizingthespecklesintissues[17].NeverthelesstheNakagamidistributionfailsinhandlinglargerimpulseresponseofthespecklesgeneratedbyhumanspecklesco