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Automatic Detection of Bioabsorbable Coronary Stents in IVUS Images Using a Cascade of Classifiers

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3 Author(s)
David Rotger ; Medical Imaging Laboratory (MILab), Computer Vision Center (CVC) and the Computer Science Department, Autonomous University of Barcelona (UAB), Barcelona, Spain ; Petia Radeva ; Nico Bruining

Bioabsorbable drug-eluting coronary stents present a very promising improvement to the common metallic ones solving some of the most important problems of stent implantation: the late restenosis. These stents made of poly-L-lactic acid cause a very subtle acoustic shadow (compared to the metallic ones) making difficult the automatic detection and measurements in images. In this paper, we propose a novel approach based on a cascade of GentleBoost classifiers to detect the stent struts using structural features to code the information of the different subregions of the struts. A stochastic gradient descent method is applied to optimize the overall performance of the detector. Validation results of struts detection are very encouraging with an average F -measure of 81%.

Published in:

IEEE Transactions on Information Technology in Biomedicine  (Volume:14 ,  Issue: 2 )