%0 Journal Article
%T Automatic Detection of Bioabsorbable Coronary Stents in IVUS Images using a Cascade of Classifiers
%A David Rotger
%A Petia Radeva
%A N. Bruining
%J IEEE Transactions on Information Technology in Biomedicine
%D 2010
%V 14
%N 2
%F David Rotger2010
%O MILAB
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=1287), last updated on Mon, 17 Feb 2014 17:29:14 +0100
%X 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%.
%U http://dx.doi.org/10.1109/TITB.2009.2017528
%P 535 – 537