Background: Intravascular ultrasound (IVUS) is an invasive imaging modality that provides high resolution cross-sectional images permitting detailed evaluation of the lumen, outer vessel wall and plaque morphology and evaluation of its composition. Over the last years several methodologies have been proposed which allow automated processing of the IVUS data and reliable segmentation of the regions of interest or characterization of the type of the plaque.
Objective: In this paper we present a novel methodology for the automated identification of different plaque components in grayscale IVUS images.
Methods: The proposed method is based on a hybrid approach that incorporates both image processing techniques and classification algorithms and allows classification of the plaque into three different categories: Hard Calcified, Hard-Non Calcified and Soft plaque. Annotations by two experts on 8 IVUS examinations were used to train and test our method.
Results: The combination of an automatic thresholding technique and active contours coupled with a Random Forest classifier provided reliable results with an overall classification accuracy of 86.14%.
Conclusions: The proposed method can accurately detect the plaque using grayscale IVUS images and can be used to assess plaque composition for both clinical and research purposes.