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

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• Publication Type: Conference Paper
• Location: Vienna, Austria
• Organization: Institute of Computer Graphics and Algorithms, Vienna University of Technology
• Keywords: Medical Visualization, Vessel Segmentation, Centerline Detection

@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. ",
  pages =      "%pages_from%--%pages_to%",
  month =      mar,
  organization = "Institute of Computer Graphics and Algorithms, Vienna
               University of Technology",
  booktitle =  "European Congres of Radiology",
  location =   "Vienna, Austria",
  keywords =   "Medical Visualization, Vessel Segmentation, Centerline
               Detection",
  URL =        "http://www.cg.tuwien.ac.at/research/publications/2004/alacruzECR2004/",
}