Obesity is an increasing problem in the western world and triggers diseases like cancer, type two diabetes, and
cardiovascular diseases. In recent years, magnetic resonance imaging (MRI) has become a clinically viable method
to measure the amount and distribution of adipose tissue (AT) in the body. However, analysis of MRI images
by manual segmentation is a tedious and time-consuming process. In this paper, we propose a semi-automatic
method to quantify the amount of different AT types from whole-body MRI data with less user interaction.
Initially, body fat is extracted by automatic thresholding. A statistical shape model of the abdomen is then
used to differentiate between subcutaneous and visceral AT. Finally, fat in the bone marrow is removed using
morphological operators. The proposed method was evaluated on 15 whole-body MRI images using manual
segmentation as ground truth for adipose tissue. The resulting overlap for total AT was 93.7% ± 5.5 with a
volumetric difference of 7.3% ± 6.4. Furthermore, we tested the robustness of the segmentation results with regard
to the initial, interactively defined position of the shape model. In conclusion, the developed method proved
suitable for the analysis of AT distribution from whole-body MRI data. For large studies, a fully automatic
version of the segmentation procedure is expected in the near future.