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.
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