2015
Matched signal detection on graphs: Theory and application to brain imaging data classification
Hu C, Sepulcre J, Johnson K, Fakhri G, Lu Y, Li Q. Matched signal detection on graphs: Theory and application to brain imaging data classification. NeuroImage 2015, 125: 587-600. PMID: 26481679, DOI: 10.1016/j.neuroimage.2015.10.026.Peer-Reviewed Original ResearchConceptsImage data classificationWeighted energy detectorGraph-signalGraph Laplacian eigenvaluesEnergy detectorManifold structureProblem of Alzheimer's diseaseData classificationGraph LaplacianSubspace detectorWeighted graphMSD approachSignal processingSignal detectionIntrinsic structureLaplacian eigenvaluesSubspaceTest statisticsGraphRandom signalsData setsLowest eigenvalueGaussian distributionTraditional methodsEigenvalues
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