Supercomputer facilities have been applied to a problem in numerically intensive medical image processing. Magnetic Resonance Imaging (MRI) data was converted into a useful information product. The motivation for this work is the "information overload" that radiologists currently experience with the overwhelming amount of data that MRI scans produce. The work was encouraged by past success in using image processing on earth observation satellite programs. The objectives of this work were to determine if the source data, multiple MRI echos, could be converted into one tissue map and to assess the computational requirements. We found that vectorizing of numerically intensive kernels reduces CPU use by a factor of 2-3 times. Our initial experience with the application of fuzzy and ISODATA clustering analysis provides data dimension reduction, improved tissue specificity, and provides a more quantitative diagnostic tool for the radiologist.