Level-set segmentation of noisy 3D images of numerically simulated blood vessels and vascular trees

The objective of this paper is to evaluate performance of the level set approach applied to segmentation and tracking of noisy 3D images of computer-simulated blood-vessel phantoms and artificial vascular trees. Of particular interest was the segmentation of thin vessels, with diameter smaller that voxel size. Flood fill technique was also explored, for comparison. Quantitative measures of segmentation accuracy were used for the methods evaluation. It was demonstrated, that the level set method provides good segmentation results even for noisy images. This promising result encourages its future application for vascularity modeling based on MR images.