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Proceedings Paper

Segmentation of magnetic resonance images of the thighs for a new National Institutes of Health initiative
Author(s): A. Monzon; P. F. Hemler; M. Nalls; T. Manini; B. C. Clark; T. B. Harris; M. J. McAuliffe
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Paper Abstract

This paper describes a new system for semi-automatically segmenting the background, subcutaneous fat, interstitial fat, muscle, bone, and bone marrow from magnetic resonance images (MRI's) of volunteers for a new osteoarthritis study. Our system first creates separate right and left thigh images from a single MR image containing both legs. The subcutaneous fat boundary is very difficult to detect in these images and is therefore interactively defined with a single boundary. The volume within the boundary is then automatically processed with a series of clustering and morphological operations designed to identify and classify the different tissue types required for this study. Once the tissues have been identified, the volume of each tissue is determined and a single, false colored, segmented image results. We quantitatively compare the segmentation in three different ways. In our first method we simply compare the tissue volumes of the resulting segmentations performed independently on both the left and right thigh. A second quantification method compares our results temporally with three image sets of the same volunteer made one month apart including a month of leg disuse. Our final quantification methodology compares the volumes of different tissues detected with our system to the results of a manual segmentation performed by a trained expert. The segmented image results of four different volunteers using images acquired at three different times suggests that the system described in this paper provides more consistent results than the manually segmented set. Furthermore, measurements of the left and right thigh and temporal results for both segmentation methods follow the anticipated trend of increasing fat and decreasing muscle over the period of disuse.

Paper Details

Date Published: 7 March 2007
PDF: 12 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65123L (7 March 2007); doi: 10.1117/12.708269
Show Author Affiliations
A. Monzon, National Institutes of Health (United States)
P. F. Hemler, Hampden-Sydney College (United States)
M. Nalls, National Institute on Aging (United States)
T. Manini, Univ. of Florida (United States)
B. C. Clark, Ohio Univ. (United States)
T. B. Harris, National Institute on Aging (United States)
M. J. McAuliffe, National Institutes of Health (United States)


Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

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