Application of Subspace Method and Sparse Coding to Tissue Characterization of Coronary Plaque for High-speed Classification

Abstract

Abstract The major cause of Acute Coronary Syndrome (ACS) is a rupture of coronary plaque. Therefore, the tissue characterization of coronary plaque is important for a diagnosis of ACS. In this study, we propose a method to use sparse features and its neighboring information obtained by a sparse coding. In the proposed method, the Radio Frequency (RF) signal obtained by the IntraVascular UltraSound (IVUS) method is expressed by a linear combination of the basis functions extracted from the learning signals by the sparse coding, and the code patterns of the expansion coeffcients of the basis functions are used for the tissue characterization. In addition, in order to perform a high-speed tissue characterization, the subspace method is employed as the classifier. The effectiveness of the proposed method has been verified by comparing the classification results of the proposed method with those of the frequency analysis-based conventional method applying to the data obtained from the human coronary arteries.