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简介：在乳房X线照片的Microcalcification簇是在乳房X线照片的microcalcifications的胸cancer.The改进的一个重要早符号是为簇microcalcifications.In的抽取的最重要的预处理技术之一这份报纸，我们在场为改进的一个新奇方法microcalcifications.Firstly，起始的microcalcification边被使用樱桃酒边操作员提取，并且discontinouse边被采用分数维的technique.Then连接，连续clo

简介：Anewwaveletvarianceanalysismethodbasedonwindowfunctionisproposedtoinvestigatethedynamicalfeaturesofelectroencephalogram(EEG).TheexprienmentalresultsshowthatthewaveletenergyofepilepticEEGsaremorediscretethannormalEEGs,andthevariationofwaveletvarianceisdifferentbetweenepilepticandnormalEEGswiththeincreaseoftime-windowwidth.Furthermore,itisfoundthatthewaveletsubbandentropy(WSE)oftheepilepticEEGsarelowerthanthenormalEEGs.

简介：Osteosarcomaisprimarymalignantneoplasmsderivedfromcellsofmesenchymalorigin,andoftenhasdistinctphenotypesatdifferentstages.Thelocationoftumorandreactionzonecanbeidentifiedbyanexpertinmagneticresonanceimaging(MRI),withMRIbeingoneofthechoicesforevaluatingtheextentofosteosarcoma.However,itisstillachallengetoautomaticallyextracttumorfromitssurroundingtissuesbecauseoftheirlowintensitydifferencesinMRI.WeinvestigatedanapproachbasedonZernikemomentandsupportvectormachine(SVM)forosteosarcomasegmentationinT1-weightedimage(TIWI).Firstly,thedifferentordermomentsaroundeachpixelarecalculatedinsmallwindows.Secondly,thegrayscaleandthemodulevaluesofdifferentordermomentsareusedasatexturefeaturevectorwhichisthenusedasthetrainingsetforSVM.Finally,anSVMclassifieristrainedbasedonthissetoffeaturestoidentifytheosteosarcoma,andthesegmentedtumortissueisrenderedin3Dbytheraycastingalgorithmbasedongraphicsprocessingunit(GPU).TheperformanceofthemethodisvalidatedonT1WI,showingthatthesegmentationmethodhasahighsimilarityindexwiththeexpert’smanualsegmentation.

简介：Basedondiscretewavelettransform,bothrelativewaveletenergy(RWE)andsegmentwaveletentropy(SWE)ofelectroencephalogram(EEG)aredefinedinthispaper.TheRWEprovidesquantitativelytheinformationabouttherelativeenergyassociatedwithdifferentfrequencybandspresentintheEEG.TheSWEcarriesinformationaboutthedegreeoforderordisorderassociatedwithdifferenttimesegmentofEEGevolution,whichcandeterminethetime-segmentlocalizationsofabnormaldynamicprocessesofbrainactivityduetothelocalizationcharacteristicsofthewavelettransform.TheexperimentalresultsshowthattheRWEandSWEaredifferentbetweenepilepticEEGsandnormalEEGs,whichdemonstratethattheRWEandtheSWEarehelpfultoanalyzethedynamicbehaviorofdifferentEEGs.

简介：这研究的目的是根据EEG信号的复杂性措施的值识别大脑的函数和状态。30件正常样品和30件耐心的样品的EEG信号是镇定的。为未加工的数据基于预处理，为复杂性措施的一个计算程序被编译，所有样品的复杂性措施是计算的。吝啬的值和控制组的复杂性措施的标准错误作为0.33和0.10，并且正常的组作为0.53和0.08。当信心度是0.05时，为控制组的复杂性措施的正常人口平均数的信心间隔是(0.2871,0.3652)，并且(0.4944,0.5552)为正常的组。正常样品和耐心的样品能清楚地是的统计结果表演由措施的价值区分了。在临床的药，结果能是是引用评估函数或状态，诊断疾病，监视大脑的康复进步。

简介：一个改进图象登记方法与混合优化器基于相互的信息被建议。第一，相互的信息措施与词法坡度信息被相结合。坡度信息的本质是有大坡度大小的地点应该被排列，而且在那些地点的坡度的取向应该是类似的。第二，一个混合优化器把PSO与鲍威尔相结合算法被建议制止相互的信息功能的本地最大值并且改进登记精确性到亚象素水平。最后，multiresolution数据结构不能仅仅基于Mallat分解改进登记功能的行为，而且改进算法的速度。试验性的结果证明新方法能产出好登记结果，比关于光滑和吸引力盆以及集中速度的传统的优化器优异。

简介：Recentinterestinmobile-basedhealthcarehasdrivensignificantdemandsonresearchingnon-contactelectrodesforelectrocardiogram(ECG)measurement.Whiletheconductivegelachievestherequirementinmakingagoodcontactbetweentheelectrodesandskin,severalproblemsappear.Agel-free,non-contactelectrodebasedoncapacitivecouplingtheorywasprovidedinthispaper,whichwasintegratedontheprintcircuitboard(PCB).TheexperimentalresultsshowedthatclearECGsignalscouldbeacquiredinthelaboratoryconditionsbycouplingtheelectrodestothechestofpatientsthroughcottonbelts.

简介：Bioluminescenceimagingisakindofemergingdetectiontechnologyatcellular,molecularandgeneticlevel.Themostpopularbioluminescenceimagingmodelisdiffusionapproximation(DA).However,becauseoftheill-posednessoftheDA-basedinverseproblemandtheinstabilityofreconstructionalgorithms,thelocationaccuracyofthereconstructedsourcesislow.Radiativetransferequation(RTE),whichconsidersthedirectionofthephotonmigrationandtheeffectofabsorptionandscatteringintissues,canaccuratelyexpressthetransmissionofbioluminescentphotonsthroughthetissues.Inthispaper,westudiedthebioluminescenceimagingbasedontheRTE.2Dsimulationswereperformed,andquantitativeevaluationwasgivenbytheabsolutesourcepositionerror,therelativesourceareaerrorandtheminimumboundingbox.TheresultsoftheexperimentshowedthattheimagingqualitybasedonRTEwasbetterthanthatonebasedonDA.

简介：Toinvestigatetherelationshipbetweensurfaceelectromyographyandsubjectiveassessmentofmusclefatigue,20youngmalevolunteersparticipatedintheexperimentofpistolholdingandaiming.sEMGoftheanteriordeltoidwasrecordedduringtheentireprocess,whilefatigueassessments(Borgscale)werecollectedevery30s.Wedividedthesignalintoseveralpartsandthenoctavebandmethodwasusedtocalculatemeanenergyofeachpart,anequationwasderivedbasedontherelationshipbetweenthemeanenergyofsEMGandBorgscale.Theresultsshowedthatthedegreeoflocalmusclefatiguecouldbedescribedbyacubiccurve,andcouldbeusedtoevaluateandmonitormusclefatigue.

简介：胎儿的ECG抽取为胎儿的监视有重要意义。这份报纸介绍基于适应线性神经网络提取胎儿的ECG的一个方法。方法能被认识到bytraining数据的小数量。另外，更好的结果能被改进神经网络结构完成。因此，更容易鉴别胎儿的ECG能被提取。试验性的结果证明适应线性神经网络能被用来有效地从母亲的腹的信号提取胎儿的ECG。而且，更清楚的胎儿的ECG能被改进神经网络结构提取。

简介：为多信道的脑电图(EEG)的独立来源分离(ISS)的一个神经网络方法在这份报纸被建议。用小浪的降噪的功能多尺度的分解，高周波的噪音从原版被移开(未加工)EEG。当加权的混合物和重量向量的表示被寻求第四顺序的cumulants的本地极值获得，然后，多信道的EEG被对待(即峭度系数)混合物。在这些进程步以后，加权的混合物被用作神经网络的输入，因此EEG的独立来源能一个一个地被分开。试验性的结果证明我们的方法为多信道的EEG的ISS是有效的。

简介：InordertoexplorethecorrelationbetweentheadjacentsegmentsofalongtermEEG,animprovedprincipalcomponentanalysis(PCA)methodbasedonmutualinformationalgorithmisproposed.Aone-dimensionEEGtimeseriesisdividedequallyintomanysegments,sothateachsegmentcanberegardedasanindependentvariablesandmulti-segmentedEEGcanbeexpressedasadatamatrix.Then,wesubstitutemutualinformationmatrixforcovariancematrixinPCAandconducttherelevanceanalysisofsegmentedEEG.Theexperimentalresultsshowthatthecontributionrateoffirstprincipalcomponent(FPC)ofsegmentedEEGismorelargerthanothers,whichcaneffectivelyreflectthedifferenceofepilepticEEGandnormalEEGwiththechangeofsegmentnumber.Inaddition,theevolutionofFPCconducetoidentifythetime-segmentlocationsofabnormaldynamicprocessesofbrainactivities,theseconclusionsarehelpfulfortheclinicalanalysisofEEG.

简介：Pulmonaryvesselsextractionisachallengingtaskinclinicalmedicine.Manypulmonarydiseasesareaccompaniedbythechangesofvesseldiameters.Thevesselsandtheirbranches,whichexhibitmuchvariability,aremostimportantinperformingdiagnosisandplanningthefollow-uptherapies.Inthispaper,weproposeanefficientapproachtopulmonaryvesselsextractionbasedonthecurveevolution.Thisapproachmodelsthevesselsasmonotonicallymarchingfrontunderthespeedfieldintegratingboththeregionandtheedgeinformationwhereanewregionspeedfunctionisdesignedandintegratedwiththeedgebasedspeedfunction.Duetotheregionbasedspeedterm,thefrontcouldevenpropagateinsmallnarrowvesselbranches.Tofurtherimprovethesegmentationresults,amulti-initialfastmarchingalgorithmisdevelopedtofastimplementthenumericalsolution,whichmayavoidthemonotonicallymarchingfrontleakingoutoftheweakboundarytooearlierandalsoreducethecomputationalcost.ThevalidityofourapproachisdemonstratedbyCTpulmonaryvesselsextraction.Experimentsshowthatthesegmentationresultsbyourapproach,especiallyonthenarrowthinvesselbranchesextraction,aremoreprecisethanthatoftheexistingmethod.

简介：Inrecentyears,thevolumetricapproachof3Dreconstructionhasemergedasapowerfulclinicmethodforbiomedicalapplications.Thisapproachisbasedonavoxelrepresentedobjectwhichmaintainsmanydetailsofthesurfaceandtheinternalcon-structuresoftheoriginalbody.Becauseofahugeamountofdata,thevolumedatasettakesalargesizeofmemory,andahighprocessingspeedisrequiredinorderto

简介：LevelSetmethodsarerobustandefficientnumericaltoolsforresolvingcurveevolutioninimagesegmentation.ThispaperproposesanewimagesegmentationalgorithmbasedonMumford-Shahmodule.ThemethodisusedtoCTimagesandtheexperimentresultsdemonstrateitsefficiencyandveracity.

简介：在这份报纸，我们使用了SVM方法检测P300信号。在为SVM训练一个分类参数前，几预处理操作包括过滤被用于数据，downsampling，单个试用抽取，windsorizing，电极选择等。与SVM算法，分类精确性能直到上面80%。在一些情况中，精确性能到达100%。在基于P300的大脑计算机接口(BCI)为P300EEG识别使用SVM是合适的系统。我们的进一步的工作将包括改进用更少试用产出更高的分类精确性。

简介：PACScanbeappliedinthenetworkarchitectureofClient/Server(C/S)andBrowser/Server(B/S)incurrentapplicationofhospitalinformationsystem.PACSViewerofB/Smodelisrealizedbymeansofthebrowsercommonly,butwebbrowserdoesnotsupportDICOMstandard.Inthisarticle,howtouseJavaApplettorealizethedisplayandoperationsofDICOMimageswillbediscussed.ThelightweightmethodtoviewDICOMimagesisveryfitfortelemedicaltreatment,andthesituationsnotrequirestrongabilityinimageoperations。

简介：介绍进散开张肌图象(DTI)的Rician噪音能在追踪的张肌计算和纤维以后带严肃的影响。减少Rician噪音的效果，我们建议认为基于小浪的散开方法降噪多信道的打的散开加权(DW)图象。当保存质地和边时，介绍变光滑的策略，在小浪领域利用各向异性的非线性的散开，成功地移开噪音。为了评估份量上，在为介绍进DW图象，peak-to-peaksignal-to-noise比率(PSNR)和signal-to-mean的Rician噪音的财务的介绍方法的效率摆平错误比率(SMSE)度量标准被采用。基于合成、真实的数据，我们计算了明显的散开系数(模数转换器)并且追踪了纤维。我们做了在介绍模型，波浪收缩和调整非线性的散开变光滑方法之间的比较。所有实验结果证明份量上并且视觉上介绍过滤器的更好的表演。

简介：Objective:Neuronsinthecochlearnucleusshowdifferentresponsepatternstotheshorttonebursts.Becauseofthelimitationsofanimalexperiments,itishardtoexploretheprinciple.Therefore,usingamodeltosimulateCNneuronswillbeafeasibleway.Methods:Basedontheinitialmodelmentionedinthepreviousstudy,weproposedanimprovedCNmodelinMATLABR2012b.Results:Bymodifyingtheparametersofthemodelwefoundtheinterchangesamong"primary-like","chopper",and"onset"responsepatterns.Furthermore,wesimulatedthe"pauser"responsepatternbyaddinganextrainputinourmodel.Conclusion:TheresultsindicatethatthesynapticintegrationsandtheinputmodescangiverisetodifferentcharacteristicsofCNneurons,whicheventuallydeterminetheresponsepatternsofCNneurons.