Prototypal software algorithms for advanced spectral analysis of echographic images were developed to perform automatic detection of simulated tumor masses at two different pathological stages. Previously published works documented the possibility of characterizing macroscopic variation of mechanical properties of tissues through elastographic techniques, using different imaging modalities, including ultrasound (US); however, the accuracy of US-based elastography remains affected by the variable manual modality of the applied compression and several attempts are under investigation to overcome this limitation. Quantitative US (QUS), such as Fourier- and wavelet-based analyses of the RF signal associated with the US images, has been developed to perform a microscopic-scale tissue-type imaging offering new solutions for operator-independent examinations. Because materials able to reproduce the harmonic behavior of human liver can be realized, in this study, tissue-mimicking structures were US imaged and the related RF signals were analyzed using wavelet transform through an in-house-developed algorithm for tissue characterization. The classification performance and reliability of the procedure were evaluated on two different tumor stiffnesses (40 and 130 kPa) and with two different applied compression levels (0 and 3.5 N). Our results demonstrated that spectral components associated with different levels of tissue stiffness within the medium exist and can be mapped onto the original US images independently of the applied compressive forces. This wavelet-based analysis was able to identify different tissue stiffness with satisfactory average sensitivity and specificity: respectively, 72.01% ± 1.70% and 81.28% ± 2.02%.