Recognition of Coronary Atherosclerotic Plaque Tissue on Intravascular Ultrasound Images by Using Misclassification Sensitive Training of Discriminative Restricted Boltzmann Machine

Abstract

Coronary atherosclerotic plaque has been extensively studied in pathological research. Improving the evaluation of vulnerable rupture is important to prevent acute heart failure. Intravascular ultrasound (IVUS) method is one of techniques to acquire information about atherosclerotic plaque, which is backscattered ultrasound signal sensed by IVUS transducer. The vessel structure and tissue components are then characterized in relation to the acquired signals. In this study, eight human coronary vessel sections are involved, and we use discriminative restricted Boltzmann machine (RBM) to classify coronary tissues as a target classifier. The quantization domain of IVUS signals are used to extract binary features for adapting Gaussian model of RBM. In addition, we propose a misclassification sensitive training of disRBM to deal with the class imbalances. The results are compared to the conventional integrated backscattered IVUS method (IB-IVUS) and the cost sensitive neural network for the same tasks.