Infiltration growth to surrounding normal tissues is a major feature of malignant lesions, which makes benign and malignant breast lesions have significant differences in the margins and surrounding tissues. Entropy is a measurement for the uncertainty in a random variable, and has been used to quantitatively analyze the margins and surrounding tissues of benign and malignant breast lesions. In the present study, the entropy based on adaptively decomposed ultrasound radio frequency (RF) data is proposed for the characterization of breast lesions tissues. Down-sampling and dilation are used to preprocess raw ultrasonic RF signals, and then the fast multivariate empirical mode decomposition (FMEMD) is used to adaptively decompose the preprocessed data into a series of intrinsic mode functions (IMFs). The ring regions of interests (ROIs) that are the areas surrounding the tumors in all individual IMFs are determined to calculate entropies, respectively. The assessment is performed on Open Access Series of Breast Ultrasonic Data (OASBUD) with the RF data of 24 benign (breast imaging reporting and data system (BI-RADS) category 3) and 24 malignant (BI-RADS category 5) breast lesions. The results demonstrate that the decomposed entropies based on IMF3 and IMF4 have the superior performance in distinguishing benign and malignant breast lesions.