Automated fat measurement and segmentation with intensity inhomogeneity correction

Daniel L. Sussman; Jianhua Yao; Ronald M. Summers

doi:10.1117/12.843860

12 March 2010 Automated fat measurement and segmentation with intensity inhomogeneity correction

Daniel L. Sussman, Jianhua Yao, Ronald M. Summers

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76233X (2010) https://doi.org/10.1117/12.843860
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

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.

Citation Download Citation

Daniel L. Sussman, Jianhua Yao, and Ronald M. Summers "Automated fat measurement and segmentation with intensity inhomogeneity correction", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76233X (12 March 2010); https://doi.org/10.1117/12.843860

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available