title: Automated segmentation of hepatic vessels in non-contrast X-ray CT images.
abstract: Hepatic-vessel trees are the key structures in the liver. Knowledge of the hepatic-vessel tree is required because it provides information for liver lesion detection in the computer-aided diagnosis (CAD) system. However, hepatic vessels cannot easily be distinguished from other liver tissues in plain CT images. Automated segmentation of hepatic vessels in plain (non-contrast) CT images is a challenging issue. In this paper, an approach to automatic segmentation of hepatic vessels is proposed. The approach consists of two processing steps: enhancement of hepatic vessels and hepatic-vessel extractions. Enhancement of the vessels was performed with two techniques: (1) histogram transformation based on a Gaussian function; (2) multi-scale line filtering based on eigenvalues of a Hessian matrix. After the enhancement of the vessels, candidates of hepatic vessels were extracted by a thresholding method. Small connected regions in the final results were considered as false positives and were removed. This approach was applied to 2 normal-liver cases for whom plain CT images were obtained. Hepatic vessels segmented from the contrast-enhanced CT images of the same patient were used as the ground truth in evaluation of the performance of the proposed approach. The index of separation ratio between the CT number distributions in hepatic vessels and other liver tissue regions was also used in the evaluation. A subjective evaluation of the hepatic-vessel extraction results based on the additional 16 plain CT cases was carried out for a further validation by a radiologist. The preliminary experimental results showed that the proposed method could enhance and segment the hepatic-vessel regions even in plain CT images.
authors.affiliation: Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, Gifu, Japan. kawajiri@fjt.info.gifu-u.ac.jp
authors.forename: Suguru
authors.initials: S
authors.lastname: Kawajiri
authors.forename: Xiangrong
authors.initials: X
authors.lastname: Zhou
authors.forename: Xuejun
authors.initials: X
authors.lastname: Zhang
authors.forename: Takeshi
authors.initials: T
authors.lastname: Hara
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Fujita
authors.forename: Ryujiro
authors.initials: R
authors.lastname: Yokoyama
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Kondo
authors.forename: Masayuki
authors.initials: M
authors.lastname: Kanematsu
authors.forename: Hiroaki
authors.initials: H
authors.lastname: Hoshi
mesh_terms: D000465:Algorithms; D003287:Contrast Media; D006499:Hepatic Artery / Q000000981:diagnostic imaging* / Q000473:pathology; D006503:Hepatic Veins / Q000000981:diagnostic imaging* / Q000473:pathology; D006801:Humans; D008099:Liver / Q000098:blood supply / Q000000981:diagnostic imaging* / Q000473:pathology; D016011:Normal Distribution; D009716:Numerical Analysis, Computer-Assisted / Q000295:instrumentation*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors; D014057:Tomography, X-Ray Computed / Q000379:methods*
chemical_list: D003287:Contrast Media
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