Segmentation and Evaluation of Adipose Tissue from Whole Body MRI Scans
Accurate quantification of total body and the distribution of regional adipose tissue using manual segmentation is a challenging problem due to the high variation between manual delineations. Manual segmentation also requires highly trained experts with knowledge of anatomy. We present a hybrid segmentation method that provides robust delineation results for adipose tissue from whole body MRI scans. A formal evaluation of accuracy of the segmentation method is performed. This semi-automatic segmentation algorithm reduces significantly the time required for quantification of adipose tissue, and the accuracy measurements show that the results are close to the ground truth obtained from manual segmentations.
Unable to display preview. Download preview PDF.
- 2.NIH Clinical Guidelines on the Identification: Evaluation and Treatment of Overweight and Obesity in Adults-the Evidence Report. Obesity Res. vol. 6, pp. 51S–209S (1998)Google Scholar
- 3.PiSunyer, F.X.: Medical Hazards of Obesity. Annals of Internal Medicine 119, 644–660 (1993)Google Scholar
- 4.Park, Y.W., Zhu, S.K., Palaniappan, L., Heshka, S., Carnethon, M.R., Heymsfield, S.B.: The metabolic syndrome: prevalence and associated risk factors. Archives and Internal Medicine (in press)Google Scholar
- 5.Ross, R., Leger, L., Guardo, R., Guise, J.D., Pike, B.G.: Adipose tissue volume measured by magnetic resonance imaging and computerized tomography in rats. J. App. Physiol. 70, 2164–2172 (1991)Google Scholar
- 10.Angelini, E., Imielinska, C., Jin, Y., Laine, A.: Improving statistics for hybrid segmentation of high-resolution multichannel images. In: SPIE Annual meeting on Medical Imaging, vol. 4684(1), pp. 401–411 (2002)Google Scholar
- 12.Udupa, J., LaBlanc, V., Schmidt, H., Imielinska, C., Saha, P., Grevera, G., Zhuge, Y., Molholt, P., Jin, Y., Currie, L.: A Methodology for Evaluating Image Segmentation Algorithm. In: SPIE Conference on Medical Imaging, San Diego CA, vol. 4684, pp. 266–277 (2002)Google Scholar
- 13.Jones, T.N., Metaxas, D.N.: Automated 3D segmentation using deformable models and fuzzy affinity. In: 15th International conference, Information Processing in Medical Imaging, Vermont, USA, pp. 113–126 (1997)Google Scholar
- 14.Jones, T.N., Metaxas, D.N.: Image Segmentation based on the integration of pixel affinity and deformable models. Computer Vision and Pattern Recognition, Sant Barbara, CA (1998)Google Scholar
- 17.Preparata, F.P., Shamos, M.I.: Computational Geometry. Springer, New York (1985)Google Scholar
- 18.Sethian, J.: Level set methods and fast marching methods (1999)Google Scholar