Guide-wire detecting using a modified cascade classifier in interventional radiology.

Endovascular surgery is becoming a widespread procedure to treat cardiovascular diseases (CVDs) such as abdominal aortic aneurysm and peripheral artery disease. The guide-wire is a crucial surgical instrument inserted into vessels to offer guidance to physicians during the surgery. There are some approaches for tracking the guide-wire, most algorithms consist of two phases, namely, the initialization phase and the tracking phase. In the initialization phase, most algorithms use B-splines for modeling the guide-wire which requires manually annotated data. In the tracking phase, the guide-wire motion is non-linearity because it is deforming and changing its shape and size as a result of patients' respiration, some algorithms decompose the non-linearity motion into rigid motion and non-rigid motion, while the computational complexity is high especially for the non-rigid motion. This paper mainly presents an approach to detect the guide-wire. The algorithm has two main advantages. First, without modeling the guide-wire, this approach uses a cascade classifier which can detect the guide-wire under arbitrary motion automatically. Second, by taking the guide-wire motion direction into consideration, the detection accuracy improves significantly. The presented work has been validated on a test set of 349 frames, and the mean tracking accuracy achieves more than 95% which proves the effectiveness of the proposed method.