2016
Numerical observer for atherosclerotic plaque classification in spectral computed tomography
Lorsakul A, Fakhri G, Worstell W, Ouyang J, Rakvongthai Y, Laine A, Li Q. Numerical observer for atherosclerotic plaque classification in spectral computed tomography. Journal Of Medical Imaging 2016, 3: 035501-035501. PMID: 27429999, PMCID: PMC4940624, DOI: 10.1117/1.jmi.3.3.035501.Peer-Reviewed Original ResearchSignal-to-noise ratioChannelized Hotelling observerMatched filterSignal-to-noise ratio improvementDual-energy CTMultienergy CTSpectral computed tomographyBinary classification taskHotelling observerNumerical observationsArea under the receiver operating characteristic curveObjective image assessmentAcquisition methodImage atherosclerotic plaquesMaterial characterizationComputed tomographyClassification taskPerformance metricsAnthropomorphic digital phantomIdentification applicationsSpectral CT dataConventional CT systemsCalcified plaqueSignal variationsAnalytical computation
2013
Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification
Hu C, Cheng L, Sepulcre J, El Fakhri G, Lu Y, Li Q. Matched Signal Detection on Graphs: Theory and Application to Brain Network Classification. Lecture Notes In Computer Science 2013, 23: 1-12. PMID: 24683953, DOI: 10.1007/978-3-642-38868-2_1.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAlzheimer DiseaseAniline CompoundsBenzothiazolesBrainBrain MappingConnectomeHumansImage EnhancementImage Interpretation, Computer-AssistedNerve NetNeural PathwaysPattern Recognition, AutomatedPositron-Emission TomographyReproducibility of ResultsSensitivity and SpecificityThiazolesTissue DistributionConceptsBrain network classificationNetwork classification problemWeighted energy detectorPrinciple component analysisSub-manifold structureTraditional principle component analysisSubspace detectionTraining dataEnergy detectorGraph structureProblem of Alzheimer's diseaseGraph LaplacianNetwork classificationNoise varianceLevel of smoothnessWeighted graphSignal detectionIntrinsic structureSignal modelGraphSubspaceIsing modelNoiseSignal variationsComponent analysis