Automatic branching detection in IVUS sequences

Authors:
Marina Alberti;Carlo Gatta;Simone Balocco;Francesco Ciompi;Oriol Pujol;Joana Silva;Xavier Carrillo;Petia Radeva
Affiliations:
Dep. of Applied Mathematics and Analysis, University of Barcelona and Computer Vision Center, Barcelona, Spain;Dep. of Applied Mathematics and Analysis, University of Barcelona and Computer Vision Center, Barcelona, Spain;Dep. of Applied Mathematics and Analysis, University of Barcelona and Computer Vision Center, Barcelona, Spain;Dep. of Applied Mathematics and Analysis, University of Barcelona and Computer Vision Center, Barcelona, Spain;Dep. of Applied Mathematics and Analysis, University of Barcelona and Computer Vision Center, Barcelona, Spain;Coimbra's Hospital Center, Cardiology Department, Coimbra, Portugal;Unitat d'hemodinèmica cardíaca, Hospital universitari "Germans Trias i Pujol", Badalona, Spain;Dep. of Applied Mathematics and Analysis, University of Barcelona and Computer Vision Center, Barcelona, Spain
Venue:
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Year:
2011

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Abstract

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.