Enhancing in-vitro IVUS data for tissue characterization

F. Ciompi, O. Pujol, O.R. Leor, C. Gatta, A.S. Vida and P. Radeva

in: Pattern Recognition and Image Analysis, 2009, pages 241-248



Intravascular Ultrasound (IVUS) data validation is usually performed by comparing post-mortem (in-vitro) IVUS data and corresponding histological analysis of the tissue, obtaining a reliable ground truth. The main drawback of this method is the few number of available study cases due to the complex procedure of histological analysis. In this work we propose a novel semi-supervised approach to enhance the in-vitro training set by including examples from in-vivo coronary plaques data set. For this purpose, a Sequential Floating Forward Selection method is applied on in-vivo data and plaque characterization performances are evaluated by Leave-One-Patient-Out cross-validation technique. Supervised data inclusion improves global classification accuracy from 89.39% to 91.82%.