Accuracy of Automated Centerline Approximation Algorithms for Lower Extremity Vessels in CTA Phantom

Alexandra La Cruz, Matús Straka, Arnold Köchl, Milos Srámek, Meister Eduard Gröller, Dominik Fleischmann
Accuracy of Automated Centerline Approximation Algorithms for Lower Extremity Vessels in CTA Phantom
In European Congres of Radiology. March 2004.

Information

Abstract

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.

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BibTeX

@inproceedings{alacruzECR2004,
  title =      "Accuracy of Automated Centerline Approximation Algorithms
               for Lower Extremity Vessels in CTA Phantom",
  author =     "Alexandra La Cruz and Mat\'{u}s Straka and Arnold K\"{o}chl
               and Milos Sr\'{a}mek and Meister Eduard Gr\"{o}ller and
               Dominik Fleischmann",
  year =       "2004",
  abstract =   "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. ",
  month =      mar,
  booktitle =  "European Congres of Radiology",
  location =   "Vienna, Austria",
  organization = "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology",
  keywords =   "Medical Visualization, Vessel Segmentation, Centerline
               Detection",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2004/alacruzECR2004/",
}