OBJECTIVES: We sought to develop a simple and robust algorithm capable of automatically detecting centerlines and bifurcations of a three-dimensional (3D) vascular bed. MATERIALS AND METHODS: After necessary preprocessing, an appropriate cost function is computed for all vessel voxels and Dijkstra's minimum-cost-path algorithm is implemented. By back tracing all the minimum-cost paths, centerlines and bifurcation are detected. The detected paths are then split into segments between adjacent nodes (bifurcations or vessel end-points) and smoothed by curve fitting. RESULTS: Application of the algorithm to both simulated 3D vessels and 3D magnetic resonance angiography (MRA) images of an actual intracranial arterial tree produced well-centered vessel skeletons. Quantitative assessment of the algorithm was performed. For the simulated data, the root mean square error for centerline detection is about half a voxel. For the human intracranial MRA data, the sensitivity, positive predictive value (PPV), and accuracy of bifurcation detection were calculated for different cost functions. The best case gave a sensitivity of 91.4%, a PPV of 91.4%, and an RMS error of 1.7 voxels. CONCLUSIONS: To the extent that imperfections are eliminated from the segmented image, the algorithm is effective and robust in automatic and accurate detection of centerlines and bifurcations. The cost function and algorithm used are demonstrated to be an improvement over similar algorithms in the literature.
OBJECTIVES: We sought to develop a simple and robust algorithm capable of automatically detecting centerlines and bifurcations of a three-dimensional (3D) vascular bed. MATERIALS AND METHODS: After necessary preprocessing, an appropriate cost function is computed for all vessel voxels and Dijkstra's minimum-cost-path algorithm is implemented. By back tracing all the minimum-cost paths, centerlines and bifurcation are detected. The detected paths are then split into segments between adjacent nodes (bifurcations or vessel end-points) and smoothed by curve fitting. RESULTS: Application of the algorithm to both simulated 3D vessels and 3D magnetic resonance angiography (MRA) images of an actual intracranial arterial tree produced well-centered vessel skeletons. Quantitative assessment of the algorithm was performed. For the simulated data, the root mean square error for centerline detection is about half a voxel. For the human intracranial MRA data, the sensitivity, positive predictive value (PPV), and accuracy of bifurcation detection were calculated for different cost functions. The best case gave a sensitivity of 91.4%, a PPV of 91.4%, and an RMS error of 1.7 voxels. CONCLUSIONS: To the extent that imperfections are eliminated from the segmented image, the algorithm is effective and robust in automatic and accurate detection of centerlines and bifurcations. The cost function and algorithm used are demonstrated to be an improvement over similar algorithms in the literature.
Authors: Karl T Diedrich; John A Roberts; Richard H Schmidt; Lisa A Cannon Albright; Anji T Yetman; Dennis L Parker Journal: AMIA Annu Symp Proc Date: 2011-10-22
Authors: Daniel R Einstein; Blazej Neradilak; Nayak Pollisar; Kevin R Minard; Chris Wallis; Michelle Fanucchi; James P Carson; Andrew P Kuprat; Senthil Kabilan; Richard E Jacob; Richard A Corley Journal: Anat Rec (Hoboken) Date: 2008-12 Impact factor: 2.064
Authors: Karl T Diedrich; John A Roberts; Richard H Schmidt; Chang-Ki Kang; Zang-Hee Cho; Dennis L Parker Journal: BMC Bioinformatics Date: 2011-10-18 Impact factor: 3.169