Abdominal tissue type differentation and quantification is increasingly becoming important in the pathophysiology of e.g. cardiovascular disease [1] and the metabolic syndrome[2,3]. However 3D image processing methods for the automated analysis of computed tomography (CT) data providing objective measurements are currently lacking. Current methods are limited to 2D[4-6], or have been developed for magnetic resonance imaging [7,8]. The purpose of this work is to develop a fully automated and intrinsically 3D image analysis method which is capable of labeling and quantifying the subcutaneous and visceral...

A three step approach is adopted: First, the patient body is separated from the background and table by applying morphological operators. Then a 3D mesh is deformed which is steered by image intensity and gradient information to delineate the abdominal region. This region is defined by the pelvis, diaphragm and the muscle layer of the abdominal wall. Finally, the subcutaneous and visceral fat are separated by thresholding using the Hounsfield units inside and outside the abdominal region.The method is applied to CTA scans of 14...

Correlations between total, visceral, subcutaneous fat volumes (TV, VV, SV) and weight, BMI and triglyceride were investigated. Significant correlations were found for (Pearson’s coefficient, only the strongest correlations are reported, significance p<0.01 for all): Weight-TV r=0.821, BMI-TV r=0.868, triglyceride-VV r=0.684.

To our knowledge we have presented the first intrinsically 3D method which is capable of fully automatically segmenting the subcutaneous and visceral fat of the abdomen in CTA. Preliminary results suggest a correlation between triglyceride and visceral fat volume.

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