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1、2006InternationalJointConferenceonNeuralNetworksSheratonVancouverWallCentreHotel,Vancouver,BC,CanadaJuly16-21,2006AReinforcementLearningFrameworkforMedicalImageSegmentationFarhangSahba,Member,IEEE,andHamidR.Tizhoosh,andMagdyM.A.Salama,Fellow,IEEEAbstractThispape
2、rintroducesanewmethodtomedicalConsideringtheabovefactorsournewalgorithmbasedonimagesegmentationusingareinforcementlearningscheme.reinforcementlearning(RL)isintroducedtolocallysegmentWeusethisnovelideaasaneffectivewaytooptimallyfindtheprostateinultrasoundimages.Th
3、emostimportantcon-theappropriatelocalthresholdingandstructuringelementceptofRLislearningbytrialanderrorbasedoninteractionvaluesandsegmenttheprostateinultrasoundimages.Re-inforcementlearningagentusesanultrasoundimageandwiththeenvironment[3],[4].ItmakestheRLagents
4、uitableitsmanuallysegmentedversionandtakessomeactions(i.e.,fordynamicenvironments.Itsgoalistofindoutanactiondifferentthresholdingandstructuringelementvalues)tochangepolicythatcontrolsthebehaviorofthedynamicprocess,theenvironment(thequalityofsegmentedimage).Theage
5、ntguidedbysignals(reinforcements)thatindicatehowwellitisprovidedwithascalarreinforcementsignaldeterminedhasbeenperformingtherequiredtask.objectively.Theagentusestheseobjectivereward/punishmenttoexplore/exploitthesolutionspace.ThevaluesobtainedusingInthecaseofapp
6、lyingthismethodtomedicalimagethiswaycanbeusedasvaluableknowledgetofillaQ-matrix.segmentation,theagenttakessomeactions(i.e.,differentThereinforcementlearningagentcanusethisknowledgeforvaluesforthresholdingandstructuringelementforamor-similarultrasoundimagesaswell.
7、Theresultsdemonstratehighphologicaloperator)tochangeitsenvironment(thequalitypotentialforapplyingreinforcementlearninginthefieldofofthesegmentedobject).Also,statesaredefinedbasedonmedicalimagesegmentation.thequalityofthissegmentedobject.First,theagenttakestheimage
8、andappliessomevalues.ThenitreceivesanobjectiveI.INTRODUCTIONrewardorpunishmentobtainedbasedoncomparisonofManyapplicationsinmedicalimagingneedtosegmentitsresultwiththe
