Computer-aided cirrhosis diagnosis via automatic liver capsule extraction and combined geometry-texture features

Liu, Xiang; Zhan, Zhi-Qin; Yan, Ming; Zhao, Jingya; Song, Jialin; Chen, Yan Qiu

doi:10.1109/icme.2017.8019551

Cited by 4 publications

(1 citation statement)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu et al [1] used the Canny operator for edge detection to generate a potential edge set, and then used dynamic programming to find the most qualified liver envelope line segment in the rectangular frame image, and obtained the complete envelope line through interpolation connection. At the same time, the continuous The morphological changes of the liver capsule were described by the two indexes of sex and smoothness, so as to classify the course of liver cirrhosis.…”

Section: Liver Capsule Extraction From Liver Imagesmentioning

confidence: 99%

Extraction of Liver Capsule from High-Frequency Ultrasound Images via Drift Iterative Search Algorithm

Zhao¹,

Jiang²,

Liu³

et al. 2023

Health

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Liver Capsule Extraction From Liver Imagesmentioning

confidence: 99%