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.
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