IEEE Transactions on Information Technology in Biomedicine · 2000 · 72 citations · 16 references
MedicinePediatricsRadiologyDiagnostic ImagingDiagnostic RadiologyPediatric RadiologyBone GrowthBone Quantity AssessmentChild GrowthGrowth AssessmentGrowth PredictionRadiographic InterpretationActive Shape ModelsGrowth Prediction Using RadiographyMorphological DescriptorsPediatric Bone ImagingPediatric MedicineRadiographic Age EstimationRadiographic Growth AssessmentSkeletal Growth Patterns
An automated knowledge-based vision system for skeletal growth estimation in children is reported in this paper. Images were obtained from hand radiographs of 32 male and 25 female children of age 1-16 yr. Phalanx bones were automatically localized and segmented using hierarchical inferences and active shape models, respectively. A number of shape descriptors were obtained from the segmented bone contour to quantify skeletal growth. From these descriptors, a feature vector was selected for a regression model and a Bayesian estimator. The estimation accuracy was 84% for females and 82% for males. This level of accuracy is comparable to that of expert pediatric radiologists, which suggests that the proposed approach has a potential application in pediatric medicine.
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Active Shape Models-Their Training and Application
T.F. Cootes, Chris Taylor, D. H. Cooper et al. · Computer Vision and Image Understanding · 1995 · 7.2K citations · Full text