8 March 2007 Automated segmentation of hepatic vessel trees in non-contrast x-ray CT images
Author Affiliations +
Abstract
Hepatic vessel trees are the key structures in the liver. Knowledge of the hepatic vessel trees is important for liver surgery planning and hepatic disease diagnosis such as portal hypertension. However, hepatic vessels cannot be easily distinguished from other liver tissues in non-contrast CT images. Automated segmentation of hepatic vessels in non-contrast CT images is a challenging issue. In this paper, an approach for automated segmentation of hepatic vessels trees in non-contrast X-ray CT images is proposed. Enhancement of hepatic vessels is performed using two techniques: (1) histogram transformation based on a Gaussian window function; (2) multi-scale line filtering based on eigenvalues of Hessian matrix. After the enhancement of hepatic vessels, candidate of hepatic vessels are extracted by thresholding. Small connected regions of size less than 100 voxels are considered as false-positives and are removed from the process. This approach is applied to 20 cases of non-contrast CT images. Hepatic vessel trees segmented from the contrast-enhanced CT images of the same patient are used as the ground truth in evaluating the performance of the proposed segmentation method. Results show that the proposed method can enhance and segment the hepatic vessel regions in non-contrast CT images correctly.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Suguru Kawajiri, Suguru Kawajiri, Xiangrong Zhou, Xiangrong Zhou, Xuejin Zhang, Xuejin Zhang, Takeshi Hara, Takeshi Hara, Hiroshi Fujita, Hiroshi Fujita, Ryujiro Yokoyama, Ryujiro Yokoyama, Hiroshi Kondo, Hiroshi Kondo, Masayuki Kanematsu, Masayuki Kanematsu, Hiroaki Hoshi, Hiroaki Hoshi, } "Automated segmentation of hepatic vessel trees in non-contrast x-ray CT images", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65123A (8 March 2007); doi: 10.1117/12.710343; https://doi.org/10.1117/12.710343
PROCEEDINGS
8 PAGES


SHARE
Back to Top