Blood Detection in IVUS Images for 3D Volume of Lumen Changes Measurement Due to Different Drugs Administration

Lumen volume variations is of great interest by the physicians given it reduces the probability of infarction as it increases. 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.