The aim of this work is to develop an automatic segmentation algorithm to classify truncular adipose tissue into different compartments. MRI acquisitions including cine-SSFP and DIXON imaging were performed at 1.5 T in 117 individuals (metabolic patients and healthy controls). Fat maps were filtered with a top-hat filter to correct intensity inhomogeneities. An active contour and a k-means algorithms were used to discriminate the SAT and the VAT. Accurate and reproducible quantification of the adipose tissue is crucial for metabolic studies since they serve as good indicators of metabolic and associated cardiovascular risks.