Abstract:Objective To investigate a set of state-of-the-art vessel enhancement algorithms and propose an automatic 3D hepatic
vascular segmentation strategy by combining the vessel enhancement and the threshold level set segmentation algorithms. Methods
Firstly, the sigmoid filter was conducted to the original 3D contrast-enhanced CT images. Then, different vessel enhancement
algorithms were analyzed and compared on the filtered images. Finally, the liver vessel was segmented by combining the threshold
level set segmentation method with the optimal vessel enhancement algorithm. Two groups of vessel enhancement algorithms were
quantitatively assessed: vesselness filters and diffusion filters. Experimental data involved 20 cases of abdominal contrast-enhanced
CT in the 3Dircadb public data sets. Results Experimental results showed that the vesselness filters were more effective than the
diffusion filters. The contrast-to-noise ratio of the vesselness filters was higher than that of the diffusion filters by 2 dB. Thus, the
diffusion filters yielded higher computational complexity and lower accuracy. The segmentation accuracy of the proposed method
was over 77%, which was higher than that of traditional segmentation algorithms including region growing, shape detection level set
and geodesic active contours. Conclusion Compared with the diffusion filters, the vesselness filters are more suitable for grey level
based on 3D hepatic vascular segmentation. The threshold level set method could alleviate the under-segmentation problem when
using only one threshold or vessel boundaries, and could segment hepatic vessels more accurately with the combination of vessel
enhancement algorithms.