Abstract
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.
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Rotger, D., Radeva, P., Fernández-Nofrerías, E., Mauri, J. (2007). Blood Detection in IVUS Images for 3D Volume of Lumen Changes Measurement Due to Different Drugs Administration. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds) Computer Analysis of Images and Patterns. CAIP 2007. Lecture Notes in Computer Science, vol 4673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74272-2_36
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DOI: https://doi.org/10.1007/978-3-540-74272-2_36
Publisher Name: Springer, Berlin, Heidelberg
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