A system (10, 200) comprising: at least one processor; and at least one computer-readable tangible storage device storing program instructions for execution by the at least one processor, the program instructions comprising: first program instructions for receiving ultrasonic derived data comprising an ultrasound image, representing a region of interest and including radio frequency "(RF") data associated with the ultrasound image, the RF data including a set of harmonic signals extracted from a frequency range associated with nonlinearly-generated second harmonics of a set of recorded echoes used to generate the ultrasound image; second program instructions for signal conditioning the RF data to deconvolve a frequency domain representation of the RF data to remove a transfer function of an ultrasound system and to adjust the frequency domain representation of the RF signals for attenuation, and for analyzing the RF data to identify at least one feature associated with the region of interest, the at least one feature including a set of features extracted from the frequency domain representation of the RF data including at least one feature representing the set of harmonic signals and including at least one of a feature representing a two-dimensional spectral feature and a feature representing a cepstrum determined from the RF data; third program instructions for classifying the region of interest as one of a plurality of anatomical classes from the identified at least one feature using a machine learning model; and fourth program instructions for causing a display to display the one of the plurality of anatomical classes. at least one processor; and at least one computer-readable tangible storage device storing program instructions for execution by the at least one processor, the program instructions comprising: first program instructions for receiving ultrasonic derived data comprising an ultrasound image, representing a region of interest and including radio frequency "(RF") data associated with the ultrasound image, the RF data including a set of harmonic signals extracted from a frequency range associated with nonlinearly-generated second harmonics of a set of recorded echoes used to generate the ultrasound image; second program instructions for signal conditioning the RF data to deconvolve a frequency domain representation of the RF data to remove a transfer function of an ultrasound system and to adjust the frequency domain representation of the RF signals for attenuation, and for analyzing the RF data to identify at least one feature associated with the region of interest, the at least one feature including a set of features extracted from the frequency domain representation of the RF data including at least one feature representing the set of harmonic signals and including at least one of a feature representing a two-dimensional spectral feature and a feature representing a cepstrum determined from the RF data; third program instructions for classifying the region of interest as one of a plurality of anatomical classes from the identified at least one feature using a machine learning model; and fourth program instructions for causing a display to display the one of the plurality of anatomical classes. first program instructions for receiving ultrasonic derived data comprising an ultrasound image, representing a region of interest and including radio frequency "(RF") data associated with the ultrasound image, the RF data including a set of harmonic signals extracted from a frequency range associated with nonlinearly-generated second harmonics of a set of recorded echoes used to generate the ultrasound image; second program instructions for signal conditioning the RF data to deconvolve a frequency domain representation of the RF data to remove a transfer function of an ultrasound system and to adjust the frequency domain representation of the RF signals for attenuation, and for analyzing the RF data to identify at least one feature associated with the region of interest, the at least one feature including a set of features extracted from the frequency domain representation of the RF data including at least one feature representing the set of harmonic signals and including at least one of a feature representing a two-dimensional spectral feature and a feature representing a cepstrum determined from the RF data; third program instructions for classifying the region of interest as one of a plurality of anatomical classes from the identified at least one feature using a machine learning model; and fourth program instructions for causing a display to display the one of the plurality of anatomical classes.