title: Coronary vessel trees from 3D imagery: a topological approach.
abstract: We propose a simple method for reconstructing vascular trees from 3D images. Our algorithm extracts persistent maxima of the intensity on all axis-aligned 2D slices of the input image. The maxima concentrate along 1D intensity ridges, in particular along blood vessels. We build a forest connecting the persistent maxima with short edges. The forest tends to approximate the blood vessels present in the image, but also contains numerous spurious features and often fails to connect segments belonging to one vessel in low contrast areas. We improve the forest by applying simple geometric filters that trim short branches, fill gaps in blood vessels and remove spurious branches from the vascular tree to be extracted. Experiments show that our technique can be applied to extract coronary trees from heart CT scans.
authors.affiliation: College of Computing, Georgia Tech, 85 5th Street NW, Atlanta, GA 30332, USA. andrzej@cc.gatech.edu
authors.forename: Andrzej
authors.initials: A
authors.lastname: Szymczak
authors.forename: Arthur
authors.initials: A
authors.lastname: Stillman
authors.forename: Allen
authors.initials: A
authors.lastname: Tannenbaum
authors.forename: Konstantin
authors.initials: K
authors.lastname: Mischaikow
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D017023:Coronary Angiography / Q000379:methods*; D003331:Coronary Vessels / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity