Digital Image Elasto Tomography (DIET) is an automated, non-invasive breast cancer screening technology with improving diagnostic potential. DIET screening involves a women lying prone with low-amplitude steady state sinusoidal vibrations applied to the free hanging breast, while surrounding cameras capture the breast surface motion. This paper presents a computationally simple diagnostic algorithm using frequency analysis of this breast surface motion data from a clinical trial using the DIET system involving N=14 women (28 breasts, 13 cancerous). Each breast was segmented into four radial and four vertical segments (16 total) and frequency decomposition of each reference point in each segment was averaged. Frequency content was hypothesised to be similar in healthy breasts among segments in the same vertical band, while stiffier cancerous tissue was hypothesised to effect frequency response, resulting in distinguishable differences and diagnostic insight. An optimal percentage tolerance, used to assess the degree of similarity between the segments, yielded 85% sensitivity and 77% specificity, showing comparable or better diagnostic accuracy than mammography. In addition, receiver operator characteristic (ROC) curve area (AUC) was 0.81, considered excellent. Diagnostic results are promising, added with the benefits of DIET screening, including portability, non-invasive screening, and no breast compression, with potential to increase screening participation and equity, improving outcomes for women.