PT Unknown
AU Marina Alberti
   Carlo Gatta
   Simone Balocco
   Francesco Ciompi
   Oriol Pujol
   Joana Silva
   Xavier Carrillo
   Petia Radeva
TI Automatic Branching Detection in IVUS Sequences
BT 5th Iberian Conference on Pattern Recognition and Image Analysis
PY 2011
BP 126
EP 133
VL 6669
DI 10.1007/978-3-642-21257-4_16
AB Atherosclerosis is a vascular pathology affecting the arterial walls, generally located in specific vessel sites, such as bifurcations. In this paper, for the first time, a fully automatic approach for the detection of bifurcations in IVUS pullback sequences is presented. The method identifies the frames and the angular sectors in which a bifurcation is visible. This goal is achieved by applying a classifier to a set of textural features extracted from each image of an IVUS pullback. A comparison between two state-of-the-art classifiers is performed, AdaBoost and Random Forest. A cross-validation scheme is applied in order to evaluate the performances of the approaches. The obtained results are encouraging, showing a sensitivity of 75% and an accuracy of 94% by using the AdaBoost algorithm.
PI Berlin
ER