Modified wavelet transform features for characterizing different plaque types in IVUS images; A feasibility study

Atherosclerosis is a leading cause of most cardiovascular diseases. Current intravascular ultrasound (IVUS) image processing techniques do not allow adequate and effective identification of the coronary artery plaques. This can be improved by defining more discriminative features for each kind of artery plaques. In this paper, the effectiveness of a modified wavelet transform feature extraction method and the Gabor filter were studied for automated characterization of the atherosclerosis plaques within the IVUS images. The methods are applied on 100 IVUS images obtained from five different patients. Support vector machine was employed in the classification step. As a result an accuracy rate of about 80% was achieved for all methods.