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Abstract
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Purpose: The accurate determination of the central vessel axis is a
prerequisite for automated visualization (curved planar reformation) and
quantitation. The purpose of this work was to assess the accuracy of
different algorithms for automated centerline detection in a phantom
simulating the peripheral arterial tree.
Methods and Material: Six algorithms were used to determine the
centerline of a synthetic peripheral arterial vessel
(aorto-to-pedal arteries, diameter 18-0.6mm) dataset (256x256x600,
voxel size 0.5x0.5x0.5mm). They are ray-casting/thresholding (RCT),
ray-casting/maximum gradient (RCMG), block matching (BM), fitting to
ellipse (FE), center of gravity (CoG), and Randomized Hough
transform (RHT). Gaussian noise whith a sigma: 0, 5 and 10 was used
to observe the accuracy of the method under noise influence The
accuracy of automatic centerline determination was quantified by
measuring the error-distance between the derived centerlines,
and the known centerline course of the synthetic dataset.
Results: BM demonstrated unacceptable performance in large
vessels (>5mm) when the shift used was less than 3 voxels. RCMG
demonstrated a greater error (mean of the error 4.73mm) in large
diameter (>15mm) vessels than in small diameter (<15mm) vessels
(mean of the error 0.64mm). Because RHT and FE use Canny edge
detector preprocessing, both are sensitive to noise. CoG and
RCT keep the mean of the error-distance significantly smaller
(0.7mm and 0.9mm respectively) than all other algorithms.
Conclusion: CoG and RCT algorithms provide the most
efficient centerline approximation over a wide range of vessel diameters.
Keywords:Centerline Detection, Vessel Segmentation, Medical Visualization
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