Classifying ultrasound image regions by using characteristics of the ARFI induced tissue displacement temporal profile

Classifying an ultrasound image into regions with diagnostic information can directly assist clinicians and can also be used as a preprocessing stage in parametric image construction. In this work we propose to extract information from an Acoustic Radiation Force Impulse (ARFI) induced displacement temporal profile and use this information as input to a linear classifier to assign each image sample to one category, either fluid, high stiffness, low stiffness or undetermined. Three parameters were derived from the displacement profile, (1) signal to noise ratio (SNR_D), (2) maximum displacement (D_max), and (3) time-to-peak displacement (TTP). The proposed method was tested on phantoms which contain a fluid filled cyst and solid inclusions with various stiffness values, two diagnostically confirmed human breast simple cysts, and five Microwave ablated thermal lesions in ex-vivo bovine livers. Using 35.6kPa as separation between low and high stiffness, for phantom and breast simple cysts we obtained a mean classification accuracy of 93%; for thermal lesions, we obtained a mean classification accuracy of 81%.