PT Journal
AU David Rotger
   Petia Radeva
   N. Bruining
TI Automatic Detection of Bioabsorbable Coronary Stents in IVUS Images using a Cascade of Classifiers
SO IEEE Transactions on Information Technology in Biomedicine
JI TITB
PY 2010
BP 535 – 537
VL 14
IS 2
DI 10.1109/TITB.2009.2017528
AB 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%.
ER