Digital Image Elasto Tomography (DIET) is a non-invasive elastographic breast cancer screening technology, relying on image-based measurement of surface vibrations induced on a breast by mechanical actuation. Knowledge of frequency response characteristics of a breast prior to imaging is critical to maximize the imaging signal and diagnostic capability of the system. A non-invasive image based modal analysis system that is designed to be able to robustly and rapidly identify resonant frequencies in soft tissue is presented in this thesis. A feasibility analysis reveals that three images per oscillation cycle are sufficient to capture the relative motion behavior at a given frequency. Moreover, the analysis suggests that 2D motion analysis is able to give an accurate estimation of the response at a particular frequency. Thus, a sweep over critical frequency ranges can be performed prior to imaging to determine critical imaging settings of the DIET system to maximize diagnositc performance. Based on feasibility simulations, a modal analysis system is presented that is based on the existing DIET digital imaging system. A frequency spectrum plot that comprises responses gathered from more than 30 different frequencies can be obtained in about 6 minutes. Preliminary results obtained from both phantom and human trials indicate that distinctive resonant frequencies can be obtained with the modal analysis system. Due to inhomogeneous properties of human breast tissues, different imaging location appear to pick up different resonances. However, there has been very limited clinical data for validating such behavior. Overall, a modal analysis system for soft tissue has been developed in this thesis. The system was first evaluated in simulation, then implemented in hardware and software, and finally successfully validated in silicone phantoms as well as human breasts.