Denatured identification of biological tissue based on ultrasonic time-frequency analysis and residual network
In order to achieve real-time and accurate denaturation identification of biological tissues during high-intensity focused ultrasound(HIFU)treatment,a new denatured identification method of biological tissue based on ultrasonic time-frequency analysis and residual network(ResNet)is proposed.Firstly,the generalized S-transformation(GST)method is used to analyze on time-frequency domain of the ultrasonic echo signal and 2D time-frequency image is obtained.Then,the parameters trained on the ImageNet dataset are applied to the ultrasonic echo signal dataset by transfer learning.Finally,the ResNet101 model is employed to learn and extract effective denatured information from the time-frequency map before and after biological tissue denaturation,visualize the characteristic trajectory,and realize the denatured identification of biological tissue in real-time.The experimental results show that compared with the existing denatured recognition methods of AR coefficient and entropy features based on signal energy,GST-ResNet method does not require artificial empirical selection of feature parameters and has higher recognition rate,which can accurately complete denatured identification of biological tissue in real-time.