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Curvilinear structures segmentation in fluoroscopic images

Abstract : Fluroscopic imaging provides means to asses the motion of the internal structures and therefore is of great use during surgery. In this report we propose a novel bottom-up approach for the segmentation of curvilinear structures in these images. The main challenge to be addressed is the lack of visual support due to the low SNR where traditional ridge-based methods fail. Our approach combines machine learning techniques, unsupervised clustering and linear programming / discrete optimization. In particular, numerous invariant to position/rotation classifiers are combined to detect candidates pixels of curvilinear structures. These candidates are grouped into consistent geometric segments through the use of a state-of-the art unsupervised clustering algorithm. The complete curvilinear structure is obtained through a linking of these segments using the elastica model in a linear programming framework in a first variant, and using discrete optimization in a second one. Very promising results were obtained on angioplasty guide wire segmentation in cardiac interventional images.
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Contributor : Nicolas Honnorat <>
Submitted on : Sunday, October 24, 2010 - 7:00:20 AM
Last modification on : Wednesday, April 8, 2020 - 3:35:51 PM
Long-term archiving on: : Tuesday, January 25, 2011 - 2:21:02 AM


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  • HAL Id : inria-00524911, version 1



Nicolas Honnorat, Régis Vaillant, Nikolaos Paragios. Curvilinear structures segmentation in fluoroscopic images. [Research Report] RR-7414, INRIA. 2010. ⟨inria-00524911⟩



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