Robust guidewire segmentation through boosting, clustering and linear programming

Authors:
Nicolas Honnorat;Régis Vaillant;Nikos Paragios
Affiliations:
Laboratoire MAS, Ecole Centrale Paris, Chatenay-Malabry, France and Equipe GALEN, INRIA Saclay-Ile-de-France, Orsay, France and General Electric Healthcare, Buc, France;General Electric Healthcare, Buc, France;Laboratoire MAS, Ecole Centrale Paris, Chatenay-Malabry, France and Equipe GALEN, INRIA Saclay-Ile-de-France, Orsay, France
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

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Abstract

Fluroscopic imaging provides means to assess the motion of the internal structures and therefore is of great use during surgery. In this paper we propose a novel 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 edge-based methods fail. Our approach combines machine learning techniques, unsupervised clustering and linear programming. In particular, numerous invariant to position/rotation classifiers are combined to detect candidate pixels of curvilinear structure. 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 an ordering of these segments using the elastica model in a linear programming framework. Very promising results were obtained on guide wire segmentation in fluoroscopic images.