Muscle functional magnetic resonance imaging (mfMRI) is frequently used to determine spatial patterns of muscle involvement in exercising humans. A frequent finding in mfMRI is that, even within synergistic muscle groups, signal intensity (SI) data from individual voxels can be quite heterogeneous. The purpose of this study was to develop a novel method for organizing heterogeneous mfMRI data into clusters whose members behave similarly to each other but distinctly from members of other clusters and apply it in studies of functional compartmentalization in the anterior compartment of the leg. An algorithm was developed that compared the SI time courses of adjacent voxels and grouped together voxels that were sufficiently similar. The algorithm's performance was verified by using simulated data sets with known regional differences in SI time courses that were then applied to experimental mfMRI data acquired from six male subjects (age 22.6 +/- 0.9 yr, mean +/- SE) who sustained isometric contractions of the dorsiflexors at 40% of maximum voluntary contraction. The experimental data were also characterized by using a traditional analysis (user-specified regions of interest from a single image), in which the relative change in SI and the contrast-to-noise ratio [CNR; 100%x(SI(RESTING) - SI(ACTIVE)/(noise standard deviation)] were measured. In general, clusters were found in areas in which the CNR exceeded 5. Cluster analysis made functional distinctions between regions of muscle that were not seen with traditional analysis. In conclusion, cluster analysis's use of the full SI time course provides more sensitivity to muscle functional compartmentation than traditional analysis.