Method and device for remote tissue identification by statistical modeling and hypothesis testing of echo ultrasound signals

摘要

For remote identification of tissue types the scattering of ultrasound energy from living tissue is modeled as an autoregressive or autoregressive moving average random process. Autoregressive or autoregressive moving average models of candidate tissue types are generated from pulse-echo data that is known to come from that particular tissue type. Kalman prediction error filters (220) are used for each candidate tissue type to generate estimates of the probability In p(Zn/Hi) that an unknown pulse echo signal belongs to the class generated by that tissue type (i). Unknown pulse-echo signals are filtered in a specific Kalman filter (220) to test the hypothesis that the unknown signal belongs to the class associated with that particular Kalman filter.