RADIO FREQUENCY SIGNAL ANALYSIS FOR TISSUE CHARACTERIZATION OF CORONARY ARTERY: IN VIVO INTRAVASCULAR ULTRASOUND STUDY

lntravascular ultrasound (IVUS) is an important clinical tool that provides high resolution cross-sectional image of coronary artery. However, it is difficult to accurately classify plaque composition by conventional IVUS images. In the present study, we apply self-organizing map (SOM) of radiofrequency (RF) signal spectra for automatic plaque classification in IVUS diagnosis. IVUS data were acquired with a commercially available IVUS system with the central frequency of 40 MHz. We used double SOM classifier. The 1st classifier is supervised-SOM, learned four structures (blood, catheter, shadow, and outer lumen) based on spectral parameters. The 2nd classifier is unsupervised-SOM, used for classifying remained data, which were not classified the 1st classifier. We defined categories on the 2nd SOM by using K-means clustering method. Finally, color codes were assigned to the plaque component values, and the tissue color coded maps were reconstructed. Results suggest that the proposed technique is useful for automatic characterization of plaque components in IVUS image.