During interventional radiology procedures, guide-wires are usually inserted into the patients vascular tree for
diagnosis or healing purpose. These procedures are monitored with an Xray interventional system providing
images of the interventional devices navigating through the patient's body. The automatic detection of such
tools by image processing means has gained maturity over the past years and enables applications ranging from
image enhancement to multimodal image fusion. Sophisticated detection methods are emerging, which rely on a
variety of device enhancement techniques. In this article we reviewed and classified these techniques into three
families. We chose a state of the art approach in each of them and built a rigorous framework to compare their
detection capability and their computational complexity. Through simulations and the intensive use of ROC
curves we demonstrated that the Hessian based methods are the most robust to strong curvature of the devices
and that the family of rotated filters technique is the most suited for detecting low CNR and low curvature
devices. The steerable filter approach demonstrated less interesting detection capabilities and appears to be the
most expensive one to compute. Finally we demonstrated the interest of automatic guide-wire detection on a
clinical topic: the compensation of respiratory motion in multimodal image fusion.
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