title: Graph based interactive detection of curve structures in 2D fluoroscopy.
abstract: An accurate and robust method to detect curve structures, such as a vessel branch or a guidewire, is essential for many medical imaging applications. A fully automatic method, although highly desired, is prone to detection errors that are caused by image noise and curve-like artifacts. In this paper, we present a novel method to interactively detect a curve structure in a 2D fluoroscopy image with a minimum requirement of human corrections. In this work, a learning based method is used to detect curve segments. Based on the detected segment candidates, a graph is built to search a curve structure as the best path passing through user interactions. Furthermore, our method introduces a novel hyper-graph based optimization method to allow for imposing geometric constraints during the path searching, and to provide a smooth and quickly converged result. With minimum human interactions involved, the method can provide accurate detection results, and has been used in different applications for guidewire and vessel detections.
authors.affiliation: Siemens Corporate Research, 755 College Road East, Princeton NJ, USA.
authors.forename: Peng
authors.initials: P
authors.lastname: Wang
authors.forename: Wei-shing
authors.initials: WS
authors.lastname: Liao
authors.forename: Terrence
authors.initials: T
authors.lastname: Chen
authors.forename: Shaohua K
authors.initials: SK
authors.lastname: Zhou
authors.forename: Dorin
authors.initials: D
authors.lastname: Comaniciu
mesh_terms: D000465:Algorithms; D005471:Fluoroscopy / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity