Before using MRI tissue segmentation in clinical studies as a dependent variable or as a means to correct functional data for differential tissue contribution, we must first establish the volume reliability and spatial distribution reproducibility of the segmentation method. Although several reports of volume reliability can be found in the literature, there are no articles assessing the reproducibility of the spatial distribution of tissue. In this report, we examine the validity, volume reliability, and spatial distribution reproducibility for our K-means cluster segmentation. Validation was examined by classifying gray matter, white matter, and CSF on images constructed using an MRI simulator and digital brain phantom, with percentage volume differences of less than 5% and spatial distribution overlaps greater than 0.94 (1.0 is perfect). We also segmented repeat scan MRIs from 10 healthy subjects, with intraclass correlation coefficients greater than 0.92 for cortical gray matter, white matter, sulcal CSF, and ventricular CSF. The original scans were also coregistered to the repeat scan of the same subject, and the spatial overlap for each tissue was then computed. Our overlaps ranged from 0.75 to 0.86 for these tissues. Our results support the use of K-means cluster segmentation, and the use of segmented structural MRIs to guide the analysis of functional and other images.