Abstract : Purpose: In this article, we present empty guiding catheter segmentation in fluoroscopic X-ray images. The guiding catheter, being a commonly visible landmark, its segmentation is an important brick for Percutaneous Coronary Intervention (PCI) procedure modeling but difficult too. Methods: In number of clinical situations, it is empty and appears as a low contrasted structure with two parallel and partially disconnected edges. To segment it, we work on the level-set scale-space of image, the min tree, to extract curve blobs. We then propose a novel structural scale-space, a hierarchy built on these curve blobs. The deep connected component , i.e. the cluster of curve blobs on this hierarchy, that maximizes the likelihood to be an empty catheter is retained as final segmentation. Results: As a result of evaluation over a database of 1250 fluoroscopic images taken from examinations of 6 patients, we obtain very good qualitative and quantitative segmentation performance, with mean precision and recall of 80.48% and 63.04% respectively. Conclusions: We develop a novel structural scale-space to segment a structured object, the empty catheter in challenging imaging situations where the information content is very sparse. Fully-automatic empty catheter segmentation in X-ray fluoroscopic images is an important and preliminary step in PCI procedure modeling, as it aids in tagging the arrival and removal location of other interventional tools.