A novel method for accurate extraction of liver capsule and auxiliary diagnosis of liver cirrhosis based on high-frequency ultrasound images.

Liver cirrhosis is a common chronic progressive disease with a high mortality rate. The early diagnosis and treatment of liver cirrhosis is an important research subject in the medical field. In this paper, a novel method is proposed for the accurate extraction of the liver capsule and auxiliary diagnosis of cirrhosis based on high frequency ultrasound images. First, a self-developed method is used to extract the predictive capsule of ultrasound images, which involves the detection of liver ascites with sliding windows, image enhancement with multiscale detail and fuzzy set, structure segmentation with morphological processing, and predictive capsule detection with traversal search method. Thereafter, the real capsule is obtained by the gray difference method according to different gray values between the liver capsule region of the original ultrasound images and the set threshold. Finally, according to the analysis of smoothness, as well as the continuity and fluctuation of predictive and real capsule, four novel features called NoL, VoS, CV, and NoF are proposed for the computer auxiliary diagnosis model. This model is designed on the basis of support vector machine and k-means clustering and can classify normal liver and three liver cirrhosis stages. The experimental results reveal that the accuracy of the liver capsule extraction using this model is 95.13% and final classification accuracy of four stages can reach 92.54%, 88.46%, 89.23% and 94.55%, respectively. The results also indicate that the method proposed in this paper can achieve the classification of liver cirrhosis stages much more accurately and efficiently compared with previously utilized methods.