Similarity Analysis Based on the Weighted Moving Window for Tissue Characterization of Plaque in Coronary Arteries

Abstract

This paper is dealing with the problem of tissue characterization of the plaque in the coronary arteries by processing the data from the intravascular ultrasound catheter. The similarity analysis method in the paper is applied in the frame of the moving window approach, which scans all cells in the matrix data from one cross section of the artery. The center-of-gravity model is used for evaluating the dissimilarity between any given pairs of data sets, belonging to pairs of windows. As a computational strategy, the use of weighted values of dissimilarity within the cells belonging to one window is proposed in the paper, rather than simply using an equal mean value for all cells in the window.

The similarity results from each cross section of the artery are displayed as gray scale image, where the darker areas denote the more similar areas to a predefined region of interest. The simulation results from the tissue characterization of a real data set show that the weighted moving window approach gives a sharper resolution of the similarity results that are closer to the real results, compared to the simple mean value approach. This suggests that the weighted moving window approach can be applied to real medical diagnosis.