摘要
Aiming at the prior medical knowledge that hepatic ascites only occurs in the severe period of liver cirrhosis, and the severe rupture of the liver capsule curve, when ascites occurs visually, can easily lead to the wrong location of the liver capsule, a transposed grayscale statistical threshold method is proposed to solve the problem. Realize the identification of liver ascites. By analyzing the visual characteristics of the liver image, the gray value of the upper half of the ultrasound image is counted column by column from a mathematical point of view, the gray distribution curve is drawn, and the relevant threshold is set for corresponding judgment. At the same time, the gray value above the ascites detection boundary is set to zero. The ablation experiment proved that the ascites detection method and post-processing operation proposed in this paper provide effective support for the precise positioning of the liver capsule curve, quantitative analysis and diagnosis of liver cirrhosis in the later stage. The Hessian matrix is sensitive to linear structure to achieve image enhancement. In view of the low accuracy of the existing liver envelope curve detection method and the incomplete quantitative evaluation of liver cirrhosis, it is proposed to use drift iteration under the synergistic effect of multiple filters. A search algorithm extracts the liver capsule.
Aiming at the prior medical knowledge that hepatic ascites only occurs in the severe period of liver cirrhosis, and the severe rupture of the liver capsule curve, when ascites occurs visually, can easily lead to the wrong location of the liver capsule, a transposed grayscale statistical threshold method is proposed to solve the problem. Realize the identification of liver ascites. By analyzing the visual characteristics of the liver image, the gray value of the upper half of the ultrasound image is counted column by column from a mathematical point of view, the gray distribution curve is drawn, and the relevant threshold is set for corresponding judgment. At the same time, the gray value above the ascites detection boundary is set to zero. The ablation experiment proved that the ascites detection method and post-processing operation proposed in this paper provide effective support for the precise positioning of the liver capsule curve, quantitative analysis and diagnosis of liver cirrhosis in the later stage. The Hessian matrix is sensitive to linear structure to achieve image enhancement. In view of the low accuracy of the existing liver envelope curve detection method and the incomplete quantitative evaluation of liver cirrhosis, it is proposed to use drift iteration under the synergistic effect of multiple filters. A search algorithm extracts the liver capsule.
作者
Jingwen Zhao
Furong Jiang
Xiang Liu
Jialin Song
Guyue Zhang
Li’na Wei
Jingwen Zhao;Furong Jiang;Xiang Liu;Jialin Song;Guyue Zhang;Li’na Wei(School of Electronic and Electric Engineering, Shanghai University of Engineering Science, Shanghai, China;Department of Ultrasound, Changzheng Hospital Affiliated to Second Military Medical University, Shanghai, China;Zhejiang Institute of Standardization, Hangzhou, China;School of Computer and Computing Science, Hangzhou City University, Hangzhou, China)
