2009
Volumetric Shape Model for Oriented Tubular Structure from DTI Data
Ho HP, Papademetris X, Wang F, Blumberg HP, Staib LH. Volumetric Shape Model for Oriented Tubular Structure from DTI Data. Lecture Notes In Computer Science 2009, 12: 18-25. PMID: 20426091, PMCID: PMC2863144, DOI: 10.1007/978-3-642-04271-3_3.Peer-Reviewed Original ResearchConceptsBrain DTI dataMagnetic resonance diffusion tensor imagesContinuous modelStatistical methodsDiffusion tensor imagesSynthetic dataShape modelBoundary findingPoint correspondencesShape priorsDTI dataShape analysisVolumetric shapeTensor imagesWhole volumePriorsShape normalizationVolumetric shape modelsModelShapeOrientation information
2002
Prior Shape Models for Boundary Finding
Staib L. Prior Shape Models for Boundary Finding. 2002, 30-33. DOI: 10.1109/isbi.2002.1029185.Peer-Reviewed Original ResearchBoundary findingTraining setAvailable training setPrior shape informationPrior informationPrior shape modelImage informationPrior shapeShape informationTarget objectBayesian formulationShape modelStatistical variationSmoothness constraintShape parametersNatural approachPosterior probabilityGeneric informationInformationObjectsAdditional flexibilitySetKey componentImagesSimilar shape
1996
Deformable boundary finding in medical images by integrating gradient and region information
Chakraborty A, Staib L, Duncan J. Deformable boundary finding in medical images by integrating gradient and region information. IEEE Transactions On Medical Imaging 1996, 15: 859-870. PMID: 18215965, DOI: 10.1109/42.544503.Peer-Reviewed Original ResearchBoundary findingMedical imagesHomogeneous region-classified areaBiomedical image analysisGray level homogeneityRegion-based segmentationReal medical imagesComputational overheadImage segmentationRegion informationShape informationPoor initializationPerceptual notionsImage analysisNumber of experimentsSegmentationVariety of limitationsGreen's theoremImagesUnified approachAuthors' approachKey issuesNew approachOverheadInformation
1994
Deformable boundary finding influenced by region homogeneity
Chakraborty A, Staib L, Duncan J. Deformable boundary finding influenced by region homogeneity. 2015 IEEE Conference On Computer Vision And Pattern Recognition (CVPR) 1994, 624-627. DOI: 10.1109/cvpr.1994.323790.Peer-Reviewed Original ResearchHomogeneous region-classified areaGray level homogeneityBoundary findingBiomedical image analysisRegion-based segmentationImage segmentationShape informationGreen's theoremPoor initializationConventional methodsPerceptual notionsRegion homogeneityImage analysisVariety of limitationsSegmentationUnified approachKey issuesAn integrated approach to boundary finding in medical images
Chakraborty A, Staib L, Duncan J. An integrated approach to boundary finding in medical images. 1994, 13-22. DOI: 10.1109/bia.1994.315870.Peer-Reviewed Original ResearchBoundary findingMedical imagesHomogeneous region-classified areaBiomedical image analysisGray level homogeneityReal medical imagesImage segmentationShape informationPoor initializationPerceptual notionsImage analysisNumber of experimentsSegmentationVariety of limitationsConventional gradientImagesUnified approachAuthors' approachKey issuesNew approachGreen's theoremConventional methodsIntegrated approachInitializationFinder
1992
Boundary finding with parametrically deformable models
Staib L, Duncan J. Boundary finding with parametrically deformable models. IEEE Transactions On Pattern Analysis And Machine Intelligence 1992, 14: 1061-1075. DOI: 10.1109/34.166621.Peer-Reviewed Original ResearchBoundary findingDeformable modelElliptic Fourier decompositionProbabilistic deformable modelGlobal shape informationShape informationSynthetic imagesOptimization problemFlexible constraintsPrior informationImage qualityIrregularity of shapeObjective functionPosteriori objective functionInformationSegmentationParametric modelProbability distributionImagesObjectsModelRepresentationConstraints
1989
Parametrically deformable contour models
Staib L, Duncan J. Parametrically deformable contour models. 2015 IEEE Conference On Computer Vision And Pattern Recognition (CVPR) 1989, 98-103. DOI: 10.1109/cvpr.1989.37834.Peer-Reviewed Original ResearchElliptic Fourier decompositionProbabilistic deformable modelVariety of imagesDeformable contour modelSegmentation problemImage dataBoundary findingShape informationDeformable modelInitial experimentationContour modelOptimization problemFlexible constraintsIrregularity of shapeBetter resultsNatural objectsSegmentationGood matchParametric modelImagesObjectsExperimentationModelInformationConstraints