Compensatory Nursing for Patients with Post-Hepatitis B Virus Cirrhosis Through Ultrasound Imaging Information

Abstract

Objective: To identify and classify the B-mode ultrasound images by utilizing the elastic algorithm of wavelet packet decomposition (WPD) and Bayesian belief network (BBN), thereby increasing the cure rate of cirrhosis by interfering the compensatory nursing for cirrhosis patients who were previously infected with hepatitis B virus (HBV). Method: First, the WPD-based elastic feature extraction algorithm (FEA), the texture FEA, the time series-based energy spectrum FEA, and the BBN are introduced. Also, the statistical feature distribution of the five fitting parameters is analyzed. Based on these three FEAs, the classification and recognition experiments of normal liver samples and liver cirrhosis samples are compared to complete the grayscale imaging of B ultrasound images. To further improve the accuracy of imaging, these elastic new features are used to build a BBN model, and the output posterior probability is used as a new parameter to realize liver ultrasound B-mode imaging. Results: Among all the classification models, the recognition rates based on the elastic new features are the highest. Especially, the average recognition rate of elastic features under random forest has reached 95%. The elastic parameters extracted from radio frequency (RF) signals obtained by conventional ultrasound diagnostic instruments are used for elastography, which can characterize the elastic information of liver tissue. Based on the elastic features, a BBN model is constructed. The pseudo-color coding of the output posterior probability of the model can characterize the stiffness of liver tissue. Also, its new parameter elastography intuitively displays the elastic information of liver tissue. Conclusion: The proposed elastic FEA of WPD and BBN have an excellent ability to identify and classify ultrasound images of cirrhosis, which can be applied in the clinical diagnosis of cirrhosis, as well as the compensatory nursing for patients with post-HBV cirrhosis.