Assessing In-vivo IVUS Tissue Classification accuracy between Normalized Image Reconstruction and RF Analysis

In this paper we present a novel framework for classification of the different kind of tissues in intravascular ultrasound (IVUS) data. We describe a normalized reconstruction process for IVUS images from radio frequency (RF) signals. The reconstructed data is described in terms of texture based features and feeds an ECOC-Adaboost learning process. In the same manner, the RF signals are characterized using Autoregressive models, and classified with a similar learning process. A comparison is performed among these techniques using two different cross validations schemes: 50 rounds of 90-10-Holdout and Leave One Patient Out obtaining very promising results.