Information
- Publication Type: Conference Paper
- Workgroup(s)/Project(s):
- Date: March 2004
- Organization: Institute of Computer Graphics and Algorithms, Vienna University of Technology
- Location: Vienna, Austria
- Booktitle: European Congres of Radiology
- Keywords: Medical Visualization, Vessel Segmentation, Centerline Detection
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.Additional Files and Images
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@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 Milo\v{s} \v{S}r\'{a}mek and 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, organization = "Institute of Computer Graphics and Algorithms, Vienna University of Technology", location = "Vienna, Austria", booktitle = "European Congres of Radiology", keywords = "Medical Visualization, Vessel Segmentation, Centerline Detection", URL = "https://www.cg.tuwien.ac.at/research/publications/2004/alacruzECR2004/", }