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.