Development of an automated 3D segmentation program for volume quantification of body fat distribution using CT.

The objective of this study was to develop a computing tool for full-automatic segmentation of body fat distributions on volumetric CT images. We developed an algorithm to automatically identify the body perimeter and the inner contour that separates visceral fat from subcutaneous fat. Diaphragmatic surfaces can be extracted by model-based segmentation to match the bottom surface of the lung in CT images for determination of the upper limitation of the abdomen. The functions for quantitative evaluation of abdominal obesity or obesity-related metabolic syndrome were implemented with a prototype three-dimensional (3D) image processing workstation. The volumetric ratios of visceral fat to total fat and visceral fat to subcutaneous fat for each subject can be calculated. Additionally, color intensity mapping of subcutaneous areas and the visceral fat layer is quite obvious in understanding the risk of abdominal obesity with the 3D surface display. Preliminary results obtained have been useful in medical checkups and have contributed to improved efficiency in checking obesity throughout the whole range of the abdomen with 3D visualization and analysis.