Obesity has become widespread in America and has been associated as a risk factor for many illnesses.
Adipose tissue (AT) content, especially visceral AT (VAT), is an important indicator for risks of many
disorders, including heart disease and diabetes. Measuring adipose tissue (AT) with traditional means is
often unreliable and inaccurate. CT provides a means to measure AT accurately and consistently. We
present a fully automated method to segment and measure abdominal AT in CT. Our method integrates
image preprocessing which attempts to correct for image artifacts and inhomogeneities. We use fuzzy cmeans
to cluster AT regions and active contour models to separate subcutaneous and visceral AT. We
tested our method on 50 abdominal CT scans and evaluated the correlations between several measurements.
Adipose tissue (AT) content, especially visceral AT (VAT), is an important indicator for risks of many disorders,
including heart disease and diabetes. Fat measurement by traditional means is often inaccurate and cannot separate
subcutaneous and visceral fat. MRI offers a medium to obtain accurate measurements and segmentation between
subcutaneous and visceral fat. We present an approach to automatically label the voxels associated with adipose tissue
and segment them between subcutaneous and visceral. Our method uses non-parametric non-uniform intensity
normalization (N3) to correct for image artifacts and inhomogeneities, fuzzy c-means to cluster AT regions and active
contour models to separate SAT and VAT. Our algorithm has four stages: body masking, preprocessing, SAT and VAT
separation, and tissue classification and quantification. The method was validated against a manual method performed by
two observers, which used thresholds and manual contours to separate SAT and VAT. We measured 25 patients, 22 of
which were included in the final analysis and the other three had too much artifact for automated processing. For SAT
and total AT, differences between manual and automatic measurements were comparable to manual inter-observer
differences. VAT measurements showed more variance in the automated method, likely due to inaccurate contours.
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