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Our purpose in this work was to develop an automatic boundary detection method for mammographic masses and to rigorously test this method via statistical analysis. The segmentation method utilized a steepest change analysis technique for determining the mass boundaries based on a composed probability density cost function. Previous investigators have shown that this function can be utilized to determine the border of the mass body. We have further analyzed this method and have discovered that the steepest changes in this function can produce mass delineations that include extended projections. The method was tested on 124 digitized mammograms selected from the University of South Florida's Digital Database for Screening Mammography (DDSM). The segmentation results were validated using overlap, accuracy, sensitivity, and specificity statistics, where the gold standards were manual traces provided by two expert radiologists. We have concluded that the best intensity threshold corresponds to a particular steepest change location within the composed probability density function. We also found that our results are more closely correlated with one expert than with the second expert. These findings were verified via Analysis of Variance (ANOVA) testing. The ANOVA tests obtained p-values ranging from 1.03 x 10(-2)-7.51 x 10(-17) for the single observer studies and 2.03 x 10(-2)-9.43 x 10(-4) for the two observer studies. Results were categorized using three significance levels, i.e., p<0.001 (extremely significant), p <0.01 (very significant), and p <0.05 (significant), respectively.