摘要
This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues(VAT and SAT)measured by magnetic resonance imaging(MRI),to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors(BSDs),and to develop a classifier to predict the fat distribution clusters using the BSDs.In the study,66 male and 54 female participants were scanned by MRI and a stereovision body imaging(SBI)to measure participants’abdominal VAT and SAT volumes and the BSDs.A fuzzy c-means algorithm was used to form the inherent grouping clusters of abdominal fat distributions.A support-vector-machine(SVM)classifier,with an embedded feature selection scheme,was employed to determine an optimal subset of the BSDs for predicting internal fat distributions.A fivefold cross-validation procedure was used to prevent over-fitting in the classification.The classification results of the BSDs were compared with those of the traditional anthropometric measurements and the Dual Energy X-Ray Absorptiometry(DXA)measurements.Four clusters were identified for abdominal fat distributions:(1)low VAT and SAT,(2)elevated VAT and SAT,(3)higher SAT,and(4)higher VAT.The cross-validation accuracies of the traditional anthropometric,DXA and BSD measurements were 85.0%,87.5% and 90%,respectively.Compared to the traditional anthropometric and DXA measurements,the BSDs appeared to be effective and efficient in predicting abdominal fat distributions.
