On Effectiveness of Subspace Method and Sparse Coding for High-Speed Tissue Classification of Coronary Plaque

Abstract

The tissue characterization of coronary plaque is important for a diagnosis of Acute Coronary Syndrome (ACS). In this study, we propose a method to use sparse features and its neighboring information obtained by a sparse coding. 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.