Automated measurement and segmentation of abdominal adipose tissue in MRI

Authors:
Daniel Lewis Sussman;Jianhua Yao;Ronald M. Summers
Affiliations:
Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD;Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD;Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

Obesity has become widespread in America and has been identified as a risk factor for many illnesses. Measuring adipose tissue (AT) with traditional means is often unreliable and inaccurate. MRI provides a safe and minimally invasive means to measure AT accurately and segment visceral AT from subcutaneous AT. However, MRI is often corrupted by image artifacts which make manual measurements difficult and time consuming. We present a fully automated method to measure and segment abdominal AT in MRI. 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 subcutaneous and visceral AT. Our method was able to measure images with severe intensity inhomogeneities and demonstrated agreement with two manual users that was close to the agreement between the manual users.