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Guide-wire Detecting Using a Modified Cascade Classifier in Interventional Radiology
Wang, Li; Xie, Xiao-Liang; Gao, Zhan-Jie; Bian, Gui-Bin; Hou, Zeng-Guang
Conference NameIEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)
Conference DateAUG 16-20, 2016
Conference PlaceOrlando, FL
AbstractEndovascular 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
Document Type会议论文
Corresponding AuthorXie, Xiao-Liang
AffiliationState Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing.
Recommended Citation
GB/T 7714
Wang, Li,Xie, Xiao-Liang,Gao, Zhan-Jie,et al. Guide-wire Detecting Using a Modified Cascade Classifier in Interventional Radiology[C],2016.
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