Coronary artery segmentation and skeletonization based on competing fuzzy connectedness tree.
We propose a new segmentation algorithm based on competing fuzzy connectedness theory, which is then used for visualizing coronary arteries in 3D CT angiography (CTA) images. The major difference compared to other fuzzy connectedness algorithms is that an additional data structure, the connectedness tree, is constructed at the same time as the seeds propagate. In preliminary evaluations, accurate result have been achieved with very limited user interaction. In addition to improving computational speed and segmentation results, the fuzzy connectedness tree algorithm also includes automated extraction of the vessel centerlines, which is a promising approach for creating curved plane reformat (CPR) images along arteries' long axes.
CMIV, Linköping University Hospital, SE-58185 Linköping, Sweden. wcl_sd@hotmail.com
Chunliang
C
Wang
Orjan
O
Smedby
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D017023:Coronary Angiography / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*