Blood Detection In IVUS Longitudinal Cuts Using AdaBoost With a Novel Feature Stability Criterion

Authors:
David Rotger;Petia Radeva;Eduard Fernández-Nofrerías;Josepa Mauri
Affiliations:
Computer Vision Center, Autonomous University of Barcelona and Computer Science Department, Autonomous University of Barcelona;Computer Vision Center, Autonomous University of Barcelona;University Hospital Germans Trias i Pujol, Badalona;University Hospital Germans Trias i Pujol, Badalona
Venue:
Proceedings of the 2007 conference on Artificial Intelligence Research and Development
Year:
2007

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Abstract

Lumen volume variations is of great interest by the physicians given the more it increases with a treatment the less probability of infarction. In this paper we present a fast and efficient method to detect the lumen borders in longitudinal cuts of IVUS sequences using an AdaBoost classifier trained with several local features assuring their stability. We propose a criterion for feature selection based on stability leave-one-out cross validation. Results on the segmentation of 18 IVUS pullbacks show that the proposed procedure is fast and robust leading to 90% of time reduction with the same characterization performance.