In studying Obstructive Sleep Apnea Syndrome (OSAS) in obese children, the quantification of obesity through MRI has been shown to be useful. For large-scale studies, interactive or manual segmentation strategies become inadequate. Our goal is to automate this process to facilitate high throughput, precision, and accuracy and to eliminate subjectivity in quantification. In this paper, we demonstrate the adaptation, to this application, of a general body-wide Automatic Anatomy Recognition (AAR) system that is being developed separately. The AAR system has been developed based on existing clinical CT image data of 50-60 year-old male subjects and using fuzzy models of a large number of objects in each body region. The individual objects and their models are arranged in a hierarchy that is specific to each body region. In the application under consideration in this paper, we are primarily interested in only the skin boundary, and subcutaneous and visceral adipose region. Further, the image modality is MRI, and the study subjects are 8-17 year-old females. We demonstrate in this paper that, once such a full AAR system is built, it can be easily adapted to a new application by specifying the objects of interest, their hierarchy, and a few other application-specific parameters. Our tests based on MRI of 14 obese subjects indicate a recognition accuracy of about 2 voxels or better for both types of adipose regions. This seems quite adequate in terms of the initialization of model-based graph-cut (GC) and iterative relative fuzzy connectedness (IRFC) algorithms implemented in our AAR system for subsequent delineation of the objects. Both algorithms achieved low false positive volume fraction (FPVF) and high true positive volume fraction (TPVF), with IRFC performing better than GC.