Liver vessel segmentation and identification based on oriented flux symmetry and graph cuts. (October 2017)

• Record Type: Journal Article
• Authors: Zeng, Ye-zhan; Zhao, Yu-qian; Tang, Ping; Liao, Miao; Liang, Yi-xiong; Liao, Sheng-hui; Zou, Bei-ji
• Is Part Of: Computer methods and programs in biomedicine. Volume 150(2017)
• Page Start: 31
• Page End: 39
• Publication Date: 2017-10
• Subjects: Liver vessel segmentation -- Optimal oriented flux (OOF) -- Oriented flux antisymmetry (OFA) -- Height ridge traversal -- Leaf node line-growing (LNLG) -- Graph cuts; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.07.002
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The combination of optimal oriented flux and oriented flux antisymmetry measures is used to yield slenderized vessels. A thin-vessel centerline extraction method is proposed based on height ridge traversal and leaf node line-growing. Our method can effectively extract thin vessels. Our method can identify hepatic vein and portal vein. Abstract: Background and objective: Accurate segmentation of liver vessels from abdominal computer tomography angiography (CTA) volume is very important for liver-vessel analysis and living-related liver transplants. This paper presents a novel liver-vessel segmentation and identification method. Methods: Firstly, an anisotropic diffusion filter is used to smooth noise while preserving vessel boundaries. Then, based on the gradient symmetry and antisymmetry pattern of vessel structures, optimal oriented flux (OOF) and oriented flux antisymmetry (OFA) measures are respectively applied to detect liver vessels and their boundaries, and further to slenderize vessels. Next, according to vessel geometrical structure, a centerline extraction measure based on height ridge traversal and leaf node line-growing (LNLG) is proposed for the extraction of liver-vessel centerlines, and an intensity model based on fast marching is integrated into graph cuts (GCs) for effective segmentation of liver vessels. Finally, a distance voting mechanism is applied to separate the hepatic vein and portal vein. Results: The experiment results on abdominal CTA images show that the proposed method can effectively segment liver vessels, achieving an average accuracy, sensitivity, and specificity of 97.7%, 79.8%, and 98.6%, respectively, and has a good performance on thin-vessel extraction. Conclusions: The proposed method does not require manual selection of the centerlines and vessel seeds, and can effectively segment liver vessels and identify hepatic vein and portal vein.